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Journal articles on the topic 'Segmentation Multimodale'
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Nai, Ying-Hwey, Bernice W. Teo, Nadya L. Tan, Koby Yi Wei Chua, Chun Kit Wong, Sophie O’Doherty, Mary C. Stephenson, et al. "Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images." Computational and Mathematical Methods in Medicine 2020 (October 20, 2020): 1–12. http://dx.doi.org/10.1155/2020/8861035.
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Prostate segmentation in multiparametric magnetic resonance imaging (mpMRI) can help to support prostate cancer diagnosis and therapy treatment. However, manual segmentation of the prostate is subjective and time-consuming. Many deep learning monomodal networks have been developed for automatic whole prostate segmentation from T2-weighted MR images. We aimed to investigate the added value of multimodal networks in segmenting the prostate into the peripheral zone (PZ) and central gland (CG). We optimized and evaluated monomodal DenseVNet, multimodal ScaleNet, and monomodal and multimodal HighRes3DNet, which yielded dice score coefficients (DSC) of 0.875, 0.848, 0.858, and 0.890 in WG, respectively. Multimodal HighRes3DNet and ScaleNet yielded higher DSC with statistical differences in PZ and CG only compared to monomodal DenseVNet, indicating that multimodal networks added value by generating better segmentation between PZ and CG regions but did not improve the WG segmentation. No significant difference was observed in the apex and base of WG segmentation between monomodal and multimodal networks, indicating that the segmentations at the apex and base were more affected by the general network architecture. The number of training data was also varied for DenseVNet and HighRes3DNet, from 20 to 120 in steps of 20. DenseVNet was able to yield DSC of higher than 0.65 even for special cases, such as TURP or abnormal prostate, whereas HighRes3DNet’s performance fluctuated with no trend despite being the best network overall. Multimodal networks did not add value in segmenting special cases but generally reduced variations in segmentation compared to the same matched monomodal network.
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Sun, Qixuan, Nianhua Fang, Zhuo Liu, Liang Zhao, Youpeng Wen, and Hongxiang Lin. "HybridCTrm: Bridging CNN and Transformer for Multimodal Brain Image Segmentation." Journal of Healthcare Engineering 2021 (October 1, 2021): 1–10. http://dx.doi.org/10.1155/2021/7467261.
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Multimodal medical image segmentation is always a critical problem in medical image segmentation. Traditional deep learning methods utilize fully CNNs for encoding given images, thus leading to deficiency of long-range dependencies and bad generalization performance. Recently, a sequence of Transformer-based methodologies emerges in the field of image processing, which brings great generalization and performance in various tasks. On the other hand, traditional CNNs have their own advantages, such as rapid convergence and local representations. Therefore, we analyze a hybrid multimodal segmentation method based on Transformers and CNNs and propose a novel architecture, HybridCTrm network. We conduct experiments using HybridCTrm on two benchmark datasets and compare with HyperDenseNet, a network based on fully CNNs. Results show that our HybridCTrm outperforms HyperDenseNet on most of the evaluation metrics. Furthermore, we analyze the influence of the depth of Transformer on the performance. Besides, we visualize the results and carefully explore how our hybrid methods improve on segmentations.
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Pan, Mingyuan, Yonghong Shi, and Zhijian Song. "Segmentation of Gliomas Based on a Double-Pathway Residual Convolution Neural Network Using Multi-Modality Information." Journal of Medical Imaging and Health Informatics 10, no. 11 (November 1, 2020): 2784–94. http://dx.doi.org/10.1166/jmihi.2020.3216.
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The automatic segmentation of brain tumors in magnetic resonance (MR) images is very important in the diagnosis, radiotherapy planning, surgical navigation and several other clinical processes. As the location, size, shape, boundary of gliomas are heterogeneous, segmenting gliomas and intratumoral structures is very difficult. Besides, the multi-center issue makes it more challenging that multimodal brain gliomas images (such as T1, T2, fluid-attenuated inversion recovery (FLAIR), and T1c images) are from different radiation centers. This paper presents a multimodal, multi-scale, double-pathway, 3D residual convolution neural network (CNN) for automatic gliomas segmentation. In the pre-processing step, a robust gray-level normalization method is proposed to solve the multi-center problem, that the intensity range from deferent centers varies a lot. Then, a doublepathway 3D architecture based on DeepMedic toolkit is trained using multi-modality information to fuse the local and context features. In the post-processing step, a fully connected conditional random field (CRF) is built to improve the performance, filling and connecting the isolated segmentations and holes. Experiments on the Multimodal Brain Tumor Segmentation (BRATS) 2017 and 2019 dataset showed that this methods can delineate the whole tumor with a Dice coefficient, a sensitivity and a positive predictive value (PPV) of 0.88, 0.89 and 0.88, respectively. As for the segmentation of the tumor core and the enhancing area, the sensitivity reached 0.80. The results indicated that this method can segment gliomas and intratumoral structures from multimodal MR images accurately, and it possesses a clinical practice value.
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Desser, Dmitriy, Francisca Assunção, Xiaoguang Yan, Victor Alves, Henrique M. Fernandes, and Thomas Hummel. "Automatic Segmentation of the Olfactory Bulb." Brain Sciences 11, no. 9 (August 28, 2021): 1141. http://dx.doi.org/10.3390/brainsci11091141.
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The olfactory bulb (OB) has an essential role in the human olfactory pathway. A change in olfactory function is associated with a change of OB volume. It has been shown to predict the prognosis of olfactory loss and its volume is a biomarker for various neurodegenerative diseases, such as Alzheimer’s disease. Thus far, obtaining an OB volume for research purposes has been performed by manual segmentation alone; a very time-consuming and highly rater-biased process. As such, this process dramatically reduces the ability to produce fair and reliable comparisons between studies, as well as the processing of large datasets. Our study aims to solve this by proposing a novel methodological framework for the unbiased measurement of OB volume. In this paper, we present a fully automated tool that successfully performs such a task, accurately and quickly. In order to develop a stable and versatile algorithm and to train the neural network, we used four datasets consisting of whole-brain T1 and high-resolution T2 MRI scans, as well as the corresponding clinical information of the subject’s smelling ability. One dataset contained data of patients suffering from anosmia or hyposmia (N = 79), and the other three datasets contained data of healthy controls (N = 91). First, the manual segmentation labels of the OBs were created by two experienced raters, independently and blinded. The algorithm consisted of the following four different steps: (1) multimodal data co-registration of whole-brain T1 images and T2 images, (2) template-based localization of OBs, (3) bounding box construction, and lastly, (4) segmentation of the OB using a 3D-U-Net. The results from the automated segmentation algorithm were tested on previously unseen data, achieving a mean dice coefficient (DC) of 0.77 ± 0.05, which is remarkably convergent with the inter-rater DC of 0.79 ± 0.08 estimated for the same cohort. Additionally, the symmetric surface distance (ASSD) was 0.43 ± 0.10. Furthermore, the segmentations produced using our algorithm were manually rated by an independent blinded rater and have reached an equivalent rating score of 5.95 ± 0.87 compared to a rating score of 6.23 ± 0.87 for the first rater’s segmentation and 5.92 ± 0.81 for the second rater’s manual segmentation. Taken together, these results support the success of our tool in producing automatic fast (3–5 min per subject) and reliable segmentations of the OB, with virtually matching accuracy with the current gold standard technique for OB segmentation. In conclusion, we present a newly developed ready-to-use tool that can perform the segmentation of OBs based on multimodal data consisting of T1 whole-brain images and T2 coronal high-resolution images. The accuracy of the segmentations predicted by the algorithm matches the manual segmentations made by two well-experienced raters. This method holds potential for immediate implementation in clinical practice. Furthermore, its ability to perform quick and accurate processing of large datasets may provide a valuable contribution to advancing our knowledge of the olfactory system, in health and disease. Specifically, our framework may integrate the use of olfactory bulb volume (OBV) measurements for the diagnosis and treatment of olfactory loss and improve the prognosis and treatment options of olfactory dysfunctions.
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Jain, Raunak, Faith Lee, Nianhe Luo, Harpreet Hyare, and Anand S. Pandit. "A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation." NeuroSci 5, no. 3 (August 2, 2024): 265–75. http://dx.doi.org/10.3390/neurosci5030021.
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The purpose of the article is to provide a practical guide for manual and semi-automated image segmentation of common neurosurgical cranial lesions, namely meningioma, glioblastoma multiforme (GBM) and subarachnoid haemorrhage (SAH), for neurosurgical trainees and researchers. Materials and Methods: The medical images used were sourced from the Medical Image Computing and Computer Assisted Interventions Society (MICCAI) Multimodal Brain Tumour Segmentation Challenge (BRATS) image database and from the local Picture Archival and Communication System (PACS) record with consent. Image pre-processing was carried out using MRIcron software (v1.0.20190902). ITK-SNAP (v3.8.0) was used in this guideline due to its availability and powerful built-in segmentation tools, although others (Seg3D, Freesurfer and 3D Slicer) are available. Quality control was achieved by employing expert segmenters to review. Results: A pipeline was developed to demonstrate the pre-processing and manual and semi-automated segmentation of patient images for each cranial lesion, accompanied by image guidance and video recordings. Three sample segmentations were generated to illustrate potential challenges. Advice and solutions were provided within both text and video. Conclusions: Semi-automated segmentation methods enhance efficiency, increase reproducibility, and are suitable to be incorporated into future clinical practise. However, manual segmentation remains a highly effective technique in specific circumstances and provides initial training sets for the development of more advanced semi- and fully automated segmentation algorithms.
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Zhu, Yuchang, and Nanfeng Xiao. "Simple Scalable Multimodal Semantic Segmentation Model." Sensors 24, no. 2 (January 22, 2024): 699. http://dx.doi.org/10.3390/s24020699.
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Visual perception is a crucial component of autonomous driving systems. Traditional approaches for autonomous driving visual perception often rely on single-modal methods, and semantic segmentation tasks are accomplished by inputting RGB images. However, for semantic segmentation tasks in autonomous driving visual perception, a more effective strategy involves leveraging multiple modalities, which is because different sensors of the autonomous driving system bring diverse information, and the complementary features among different modalities enhance the robustness of the semantic segmentation modal. Contrary to the intuitive belief that more modalities lead to better accuracy, our research reveals that adding modalities to traditional semantic segmentation models can sometimes decrease precision. Inspired by the residual thinking concept, we propose a multimodal visual perception model which is capable of maintaining or even improving accuracy with the addition of any modality. Our approach is straightforward, using RGB as the main branch and employing the same feature extraction backbone for other modal branches. The modals score module (MSM) evaluates channel and spatial scores of all modality features, measuring their importance for overall semantic segmentation. Subsequently, the modal branches provide additional features to the RGB main branch through the features complementary module (FCM). Leveraging the residual thinking concept further enhances the feature extraction capabilities of all the branches. Through extensive experiments, we derived several conclusions. The integration of certain modalities into traditional semantic segmentation models tends to result in a decline in segmentation accuracy. In contrast, our proposed simple and scalable multimodal model demonstrates the ability to maintain segmentation precision when accommodating any additional modality. Moreover, our approach surpasses some state-of-the-art multimodal semantic segmentation models. Additionally, we conducted ablation experiments on the proposed model, confirming that the application of the proposed MSM, FCM, and the incorporation of residual thinking contribute significantly to the enhancement of the model.
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Farag, A. A., A. S. El-Baz, and G. Gimel'farb. "Precise segmentation of multimodal images." IEEE Transactions on Image Processing 15, no. 4 (April 2006): 952–68. http://dx.doi.org/10.1109/tip.2005.863949.
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You, Siming. "Deep learning in autonomous driving: Advantages, limitations, and innovative solutions." Applied and Computational Engineering 75, no. 1 (July 5, 2024): 147–53. http://dx.doi.org/10.54254/2755-2721/75/20240528.
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With the rapid development of autonomous driving technology, deep learning has become a core driver for innovation in testing autonomous driving scenarios. This review paper delves into the critical role of deep learning in autonomous driving technology. The paper will describe how deep learning is at the center of driving innovation. The paper thoroughly explores the application of deep learning in obstacle detection, scene classification and understanding, and image segmentation, emphasizing the significant benefits in perception and decision-making while pointing out the challenges and innovative solutions adopted. The innovative solutions section proposes multimodal fusion and joint learning, new methods for 3D semantic segmentation, etc., aiming to improve image segmentation's accuracy and generalization ability. Overall, deep learning has great potential in automated driving technology, and by innovating and solving challenges, it will advance the system and provide reliable, intelligent, and efficient solutions for future transportation systems.
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Zuo, Qiang, Songyu Chen, and Zhifang Wang. "R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation." Security and Communication Networks 2021 (June 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/6625688.
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In recent years, semantic segmentation method based on deep learning provides advanced performance in medical image segmentation. As one of the typical segmentation networks, U-Net is successfully applied to multimodal medical image segmentation. A recurrent residual convolutional neural network with attention gate connection (R2AU-Net) based on U-Net is proposed in this paper. It enhances the capability of integrating contextual information by replacing basic convolutional units in U-Net by recurrent residual convolutional units. Furthermore, R2AU-Net adopts attention gates instead of the original skip connection. In this paper, the experiments are performed on three multimodal datasets: ISIC 2018, DRIVE, and public dataset used in LUNA and the Kaggle Data Science Bowl 2017. Experimental results show that R2AU-Net achieves much better performance than other improved U-Net algorithms for multimodal medical image segmentation.
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Zhang, Yong, Yu-mei Zhou, Zhen-hong Liao, Gao-yuan Liu, and Kai-can Guo. "Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images." Journal of Healthcare Engineering 2021 (February 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/5573010.
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In order to improve the accuracy of glioma segmentation, a multimodal MRI glioma segmentation algorithm based on superpixels is proposed. Aiming at the current unsupervised feature extraction methods in MRI brain tumor segmentation that cannot adapt to the differences in brain tumor images, an MRI brain tumor segmentation method based on multimodal 3D convolutional neural networks (CNNs) feature extraction is proposed. First, the multimodal MRI is oversegmented into a series of superpixels that are uniform, compact, and exactly fit the image boundary. Then, a dynamic region merging algorithm based on sequential probability ratio hypothesis testing is applied to gradually merge the generated superpixels to form dozens of statistically significant regions. Finally, these regions are postprocessed to obtain the segmentation results of each organization of GBM. Combine 2D multimodal MRI images into 3D original features and extract features through 3D-CNNs, which is more conducive to extracting the difference information between the modalities, removing redundant interference information between the modalities, and reducing the original features at the same time. The size of the neighborhood can adapt to the difference of tumor size in different image layers of the same patient and further improve the segmentation accuracy of MRI brain tumors. The experimental results prove that it can adapt to the differences and variability between the modalities of different patients to improve the segmentation accuracy of brain tumors.
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Cai, Shengliang, Chuyun Shen, and Xiangfeng Wang. "Energy-Based MRI Semantic Augmented Segmentation for Unpaired CT Images." Electronics 12, no. 10 (May 10, 2023): 2174. http://dx.doi.org/10.3390/electronics12102174.
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The multimodal segmentation of medical images is essential for clinical applications as it allows medical professionals to detect anomalies, monitor treatment effectiveness, and make informed therapeutic decisions. However, existing segmentation methods depend on paired images of modalities, which may not always be available in practical scenarios, thereby limiting their applicability. To address this challenge, current approaches aim to align modalities or generate missing modality images without a ground truth, which can introduce irrelevant texture details. In this paper, we propose the energy-basedsemantic augmented segmentation (ESAS) model, which employs the energy of latent semantic features from a supporting modality to enhance the segmentation performance on unpaired query modality data. The proposed ESAS model is a lightweight and efficient framework suitable for most unpaired multimodal image-learning tasks. We demonstrate the effectiveness of our ESAS model on the MM-WHS 2017 challenge dataset, where it significantly improved Dice accuracy for cardiac segmentation on CT volumes. Our results highlight the potential of the proposed ESAS model to enhance patient outcomes in clinical settings by providing a promising approach for unpaired multimodal medical image segmentation tasks.
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Lu, Yisu, Jun Jiang, Wei Yang, Qianjin Feng, and Wufan Chen. "Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior." Computational and Mathematical Methods in Medicine 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/717206.
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Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use.
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Aganj, Iman, and Bruce Fischl. "Multimodal Image Registration Through Simultaneous Segmentation." IEEE Signal Processing Letters 24, no. 11 (November 2017): 1661–65. http://dx.doi.org/10.1109/lsp.2017.2754263.
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Gao, Suining, Xiubin Yang, Li Jiang, Zongqiang Fu, and Jiamin Du. "Global feature-based multimodal semantic segmentation." Pattern Recognition 151 (July 2024): 110340. http://dx.doi.org/10.1016/j.patcog.2024.110340.
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Tian Hengyi, 田恒屹, 王瑜 Wang Yu, and 肖洪兵 Xiao Hongbing. "基于多模态特征重组和尺度交叉注意力机制的全自动脑肿瘤分割算法." Chinese Journal of Lasers 51, no. 21 (2024): 2107110. http://dx.doi.org/10.3788/cjl240779.
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Pan, Yanyan, Huiping Zhang, Jinsuo Yang, Jing Guo, Zhiguo Yang, Jianbing Wang, and Ge Song. "Identification and Diagnosis of Cerebral Stroke through Deep Convolutional Neural Network-Based Multimodal MRI Images." Contrast Media & Molecular Imaging 2021 (July 20, 2021): 1–8. http://dx.doi.org/10.1155/2021/7598613.
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This study aimed to explore the application value of multimodal magnetic resonance imaging (MRI) images based on the deep convolutional neural network (Conv.Net) in the diagnosis of strokes. Specifically, four automatic segmentation algorithms were proposed to segment multimodal MRI images of stroke patients. The segmentation effects were evaluated factoring into DICE, accuracy, sensitivity, and segmentation distance coefficient. It was found that although two-dimensional (2D) full convolutional neural network-based segmentation algorithm can locate and segment the lesion, its accuracy was low; the three-dimensional one exhibited higher accuracy, with various objective indicators improved, and the segmentation accuracy of the training set and the test set was 0.93 and 0.79, respectively, meeting the needs of automatic diagnosis. The asymmetric 3D residual U-Net network had good convergence and high segmentation accuracy, and the 3D deep residual network proposed on its basis had good segmentation coefficients, which can not only ensure segmentation accuracy but also avoid network degradation problems. In conclusion, the Conv.Net model can accurately segment the foci of patients with ischemic stroke and is suggested in clinic.
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Skokan, M., L. Kubečka, M. Wolf, K. Donath, J. Jan, G. Michelson, H. Niemann, and R. Chrástek. "Multimodal Retinal Image Registration for Optic Disk Segmentation." Methods of Information in Medicine 43, no. 04 (2004): 336–42. http://dx.doi.org/10.1055/s-0038-1633888.
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Summary Objectives: The analysis of the optic disk morphology with the means of the scanning laser tomography is an important step for glaucoma diagnosis. A method we developed for optic disk segmentation in images of the scanning laser tomograph is limited by noise, nonuniform illumination and presence of blood vessels. Inspired by recent medical research, we wanted to develop a tool for improving optic disk segmentation by registration of images of the scanning laser tomograph and color fundus photographs and by applying a method we developed for optic disk segmentation in color fundus photographs. Methods: The segmentation of the optic disk for glaucoma diagnosis in images of the scanning laser tomograph is based on morphological operations, detection of anatomical structures and active contours and has been described in a previous paper [1]. The segmentation of the optic disk in the fundus photographs is based on nonlinear filtering, Canny edge detector and a modified Hough transform. The registration is based on mutual information using simulated annealing for finding maxima. Results: The registration was successful 86.8% of the time when tested on 174 images. Results of the registration have shown a very low displacement error of a maximum of about 5 pixels. The correctness of the registration was manually evaluated by measuring distances between the real vessel borders and those from the registered image. Conclusions: We have developed a method for the registration of images of the scanning laser tomograph and fundus photographs. Our first experiments showed that the optic disk segmentation could be improved by fused information from both image modalities.
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Bougacha, Aymen, Ines Njeh, Jihene Boughariou, Omar Kammoun, Kheireddine Ben Mahfoudh, Mariem Dammak, Chokri Mhiri, and Ahmed Ben Hamida. "Rank-Two NMF Clustering for Glioblastoma Characterization." Journal of Healthcare Engineering 2018 (October 23, 2018): 1–7. http://dx.doi.org/10.1155/2018/1048164.
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This study investigates a novel classification method for 3D multimodal MRI glioblastomas tumor characterization. We formulate our segmentation problem as a linear mixture model (LMM). Thus, we provide a nonnegative matrix M from every MRI slice in every segmentation process’ step. This matrix will be used as an input for the first segmentation process to extract the edema region from T2 and FLAIR modalities. After that, in the rest of segmentation processes, we extract the edema region from T1c modality, generate the matrix M, and segment the necrosis, the enhanced tumor, and the nonenhanced tumor regions. In the segmentation process, we apply a rank-two NMF clustering. We have executed our tumor characterization method on BraTS 2015 challenge dataset. Quantitative and qualitative evaluations over the publicly training and testing dataset from the MICCAI 2015 multimodal brain segmentation challenge (BraTS 2015) attested that the proposed algorithm could yield a competitive performance for brain glioblastomas characterization (necrosis, tumor core, and edema) among several competing methods.
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Zhou, Runwei, Shijun Hu, Baoxiang Ma, and Bangcheng Ma. "Automatic Segmentation of MRI of Brain Tumor Using Deep Convolutional Network." BioMed Research International 2022 (June 15, 2022): 1–9. http://dx.doi.org/10.1155/2022/4247631.
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Computer-aided diagnosis and treatment of multimodal magnetic resonance imaging (MRI) brain tumor image segmentation has always been a hot and significant topic in the field of medical image processing. Multimodal MRI brain tumor image segmentation utilizes the characteristics of each modal in the MRI image to segment the entire tumor and tumor core area and enhanced them from normal brain tissues. However, the grayscale similarity between brain tissues in various MRI images is very immense making it difficult to deal with the segmentation of multimodal MRI brain tumor images through traditional algorithms. Therefore, we employ the deep learning method as a tool to make full use of the complementary feature information between the multimodalities and instigate the following research: (i) build a network model suitable for brain tumor segmentation tasks based on the fully convolutional neural network framework and (ii) adopting an end-to-end training method, using two-dimensional slices of MRI images as network input data. The problem of unbalanced categories in various brain tumor image data is overcome by introducing the Dice loss function into the network to calculate the network training loss; at the same time, parallel Dice loss is proposed to further improve the substructure segmentation effect. We proposed a cascaded network model based on a fully convolutional neural network to improve the tumor core area and enhance the segmentation accuracy of the tumor area and achieve good prediction results for the substructure segmentation on the BraTS 2017 data set.
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Liu, Kang, and Xin Gao. "Multiscale Efficient Channel Attention for Fusion Lane Line Segmentation." Complexity 2021 (December 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/6791882.
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The use of multimodal sensors for lane line segmentation has become a growing trend. To achieve robust multimodal fusion, we introduced a new multimodal fusion method and proved its effectiveness in an improved fusion network. Specifically, a multiscale fusion module is proposed to extract effective features from data of different modalities, and a channel attention module is used to adaptively calculate the contribution of the fused feature channels. We verified the effect of multimodal fusion on the KITTI benchmark dataset and A2D2 dataset and proved the effectiveness of the proposed method on the enhanced KITTI dataset. Our method achieves robust lane line segmentation, which is 4.53% higher than the direct fusion on the precision index, and obtains the highest F2 score of 79.72%. We believe that our method introduces an optimization idea of modal data structure level for multimodal fusion.
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Du, Xufeng, and Yaye He. "Application of CT Multimodal Images in Rehabilitation Monitoring of Long-Distance Running." Scanning 2022 (October 4, 2022): 1–7. http://dx.doi.org/10.1155/2022/6425448.
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In order to monitor the rehabilitation of athletes injured in long-distance running, the author proposes a method for rehabilitation monitoring of long-distance running based on CT multimodal images. This method combines the latest multimodal image technology, integrates multimodal technology into CT images to improve the accuracy, performs image segmentation on CT multimodal images through medical segmentation methods, and analyzes the segmented images; finally, it can achieve the effect of rehabilitation treatment for athletes in long-distance running. Experimental results show that the total time taken by the authors’ method is 10.9 hours, with an average time of 8 seconds, which is much shorter than the other two control methods. In conclusion, the authors’ method allows for better rehabilitation monitoring of long-distance running sports injuries.
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Bodani, P., K. Shreshtha, and S. Sharma. "ORTHOSEG: A DEEP MULTIMODAL CONVOLUTONAL NEURAL NETWORK ARCHITECTURE FOR SEMANTIC SEGMENTATION OF ORTHOIMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 621–28. http://dx.doi.org/10.5194/isprs-archives-xlii-5-621-2018.
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<p><strong>Abstract.</strong> This paper addresses the task of semantic segmentation of orthoimagery using multimodal data e.g. optical RGB, infrared and digital surface model. We propose a deep convolutional neural network architecture termed OrthoSeg for semantic segmentation using multimodal, orthorectified and coregistered data. We also propose a training procedure for supervised training of OrthoSeg. The training procedure complements the inherent architectural characteristics of OrthoSeg for preventing complex co-adaptations of learned features, which may arise due to probable high dimensionality and spatial correlation in multimodal and/or multispectral coregistered data. OrthoSeg consists of parallel encoding networks for independent encoding of multimodal feature maps and a decoder designed for efficiently fusing independently encoded multimodal feature maps. A softmax layer at the end of the network uses the features generated by the decoder for pixel-wise classification. The decoder fuses feature maps from the parallel encoders locally as well as contextually at multiple scales to generate per-pixel feature maps for final pixel-wise classification resulting in segmented output. We experimentally show the merits of OrthoSeg by demonstrating state-of-the-art accuracy on the ISPRS Potsdam 2D Semantic Segmentation dataset. Adaptability is one of the key motivations behind OrthoSeg so that it serves as a useful architectural option for a wide range of problems involving the task of semantic segmentation of coregistered multimodal and/or multispectral imagery. Hence, OrthoSeg is designed to enable independent scaling of parallel encoder networks and decoder network to better match application requirements, such as the number of input channels, the effective field-of-view, and model capacity.</p>
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Martin, R. John, Uttam Sharma, Kiranjeet Kaur, Noor Mohammed Kadhim, Madonna Lamin, and Collins Sam Ayipeh. "Multidimensional CNN-Based Deep Segmentation Method for Tumor Identification." BioMed Research International 2022 (August 21, 2022): 1–11. http://dx.doi.org/10.1155/2022/5061112.
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Weighted MR images of 421 patients with nasopharyngeal cancer were obtained at the head and neck level, and the tumors in the images were assessed by two expert doctors. 346 patients’ multimodal pictures and labels served as training sets, whereas the remaining 75 patients’ multimodal images and labels served as independent test sets. Convolutional neural network (CNN) for modal multidimensional information fusion and multimodal multidimensional information fusion (MMMDF) was used. The three models’ performance is compared, and the findings reveal that the multimodal multidimensional fusion model performs best, while the two-modal multidimensional information fusion model performs second. The single-modal multidimensional information fusion model has the poorest performance. In MR images of nasopharyngeal cancer, a convolutional network can precisely and efficiently segment tumors.
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Wang, Suzhe, Xueying Zhang, Haisheng Hui, Fenglian Li, and Zelin Wu. "Multimodal CT Image Synthesis Using Unsupervised Deep Generative Adversarial Networks for Stroke Lesion Segmentation." Electronics 11, no. 16 (August 20, 2022): 2612. http://dx.doi.org/10.3390/electronics11162612.
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Deep learning-based techniques can obtain high precision for multimodal stroke segmentation tasks. However, the performance often requires a large number of training examples. Additionally, existing data extension approaches for the segmentation are less efficient in creating much more realistic images. To overcome these limitations, an unsupervised adversarial data augmentation mechanism (UTC-GAN) is developed to synthesize multimodal computed tomography (CT) brain scans. In our approach, the CT samples generation and cross-modality translation differentiation are accomplished simultaneously by integrating a Siamesed auto-encoder architecture into the generative adversarial network. In addition, a Gaussian mixture translation module is further proposed, which incorporates a translation loss to learn an intrinsic mapping between the latent space and the multimodal translation function. Finally, qualitative and quantitative experiments show that UTC-GAN significantly improves the generation ability. The stroke dataset enriched by the proposed model also provides a superior improvement in segmentation accuracy, compared with the performance of current competing unsupervised models.
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Moitra, D. "COMPARISON OF MULTIMODAL TUMOR IMAGE SEGMENTATION TECHNIQUES." International Journal of Advanced Research in Computer Science 9, no. 3 (June 20, 2018): 129–31. http://dx.doi.org/10.26483/ijarcs.v9i3.6010.
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Méndez, C. Andrés, Paul Summers, and Gloria Menegaz. "Multiview cluster ensembles for multimodal MRI segmentation." International Journal of Imaging Systems and Technology 25, no. 1 (February 9, 2015): 56–67. http://dx.doi.org/10.1002/ima.22121.
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Spaide, Theodore, Jiaxiang Jiang, Jasmine Patil, Neha Anegondi, Verena Steffen, Michael G. Kawczynski, Elizabeth M. Newton, et al. "Geographic Atrophy Segmentation Using Multimodal Deep Learning." Translational Vision Science & Technology 12, no. 7 (July 10, 2023): 10. http://dx.doi.org/10.1167/tvst.12.7.10.
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Stich, Manuel, Jeannine Vogt, Michaela Lindner, and Ralf Ringler. "Implementation and evaluation of segmentation algorithms according to multimodal imaging in personalized medicine." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 207–10. http://dx.doi.org/10.1515/cdbme-2017-0178.
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AbstractMultimodal imaging is gaining in importance in the field of personalized medicine and can be described as a current trend in medical imaging diagnostics. The segmentation, classification and analysis of tissue structures is essential. The goal of this study is the evaluation of established segmentation methods on different medical image data sets acquired with different diagnostic procedures. Established segmentation methods were implemented using the latest state of the art and applied to medical image data sets. In order to benchmark the segmentation performance quantitatively, medical image data sets were superimposed with artificial Gaussian noise, and the mis-segmentation as a function of the image SNR or CNR was compared to a gold standard. The evaluation of the image segmentation showed that the best results of pixel-based segmentation (< 3%) can be achieved with methods of machine learning, multithreshold and advanced level-set method - even at high artificial noise (SNR< 18). Finally, the complexity of the object geometry and the contrast of the ROI to the surrounding tissue must also be considered to select the best segmentation algorithm.
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Qian, Zhuliang, Lifeng Xie, and Yisheng Xu. "3D Automatic Segmentation of Brain Tumor Based on Deep Neural Network and Multimodal MRI Images." Emergency Medicine International 2022 (August 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/5356069.
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Brain tumor segmentation is an important content in medical image processing, and it is also a very common research in medicine. Due to the development of modern technology, it is very valuable to use deep learning (DL) and multimodal MRI images to study brain tumor segmentation. In order to solve the problems of low efficiency and low accuracy of brain tumor segmentation, this paper proposes DL to conduct research on multimodal MRI image segmentation, aiming to make accurate diagnosis and treatment for doctors. In addition, this paper constructs an automatic diagnosis system for brain tumors, uses GLCM and discrete wavelet transform (DWT) to extract features from MRI images, and then uses a convolutional neural network (CNN) for final diagnosis; finally, through four. The comparison of the results between the two algorithms proves that the CNN algorithm has the better processing power and higher efficiency.
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Ademaj, Adela, Lavdie Rada, Mazlinda Ibrahim, and Ke Chen. "A variational joint segmentation and registration framework for multimodal images." Journal of Algorithms & Computational Technology 14 (January 2020): 174830262096669. http://dx.doi.org/10.1177/1748302620966691.
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Image segmentation and registration are closely related image processing techniques and often required as simultaneous tasks. In this work, we introduce an optimization-based approach to a joint registration and segmentation model for multimodal images deformation. The model combines an active contour variational term with mutual information (MI) smoothing fitting term and solves in this way the difficulties of simultaneously performed segmentation and registration models for multimodal images. This combination takes into account the image structure boundaries and the movement of the objects, leading in this way to a robust dynamic scheme that links the object boundaries information that changes over time. Comparison of our model with state of art shows that our method leads to more consistent registrations and accurate results.
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Hervella, Álvaro S., Lucía Ramos, José Rouco, Jorge Novo, and Marcos Ortega. "Joint Optic Disc and Cup Segmentation Using Self-Supervised Multimodal Reconstruction Pre-Training." Proceedings 54, no. 1 (August 20, 2020): 25. http://dx.doi.org/10.3390/proceedings2020054025.
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The analysis of the optic disc and cup in retinal images is important for the early diagnosis of glaucoma. In order to improve the joint segmentation of these relevant retinal structures, we propose a novel approach applying the self-supervised multimodal reconstruction of retinal images as pre-training for deep neural networks. The proposed approach is evaluated on different public datasets. The obtained results indicate that the self-supervised multimodal reconstruction pre-training improves the performance of the segmentation. Thus, the proposed approach presents a great potential for also improving the interpretable diagnosis of glaucoma.
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Liu, Qiang, Enqing Chen, Lei Gao, Chengwu Liang, and Hao Liu. "Energy-Guided Temporal Segmentation Network for Multimodal Human Action Recognition." Sensors 20, no. 17 (August 19, 2020): 4673. http://dx.doi.org/10.3390/s20174673.
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To achieve the satisfactory performance of human action recognition, a central task is to address the sub-action sharing problem, especially in similar action classes. Nevertheless, most existing convolutional neural network (CNN)-based action recognition algorithms uniformly divide video into frames and then randomly select the frames as inputs, ignoring the distinct characteristics among different frames. In recent years, depth videos have been increasingly used for action recognition, but most methods merely focus on the spatial information of the different actions without utilizing temporal information. In order to address these issues, a novel energy-guided temporal segmentation method is proposed here, and a multimodal fusion strategy is employed with the proposed segmentation method to construct an energy-guided temporal segmentation network (EGTSN). Specifically, the EGTSN had two parts: energy-guided video segmentation and a multimodal fusion heterogeneous CNN. The proposed solution was evaluated on a public large-scale NTU RGB+D dataset. Comparisons with state-of-the-art methods demonstrate the effectiveness of the proposed network.
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Xiong, Feng, Chuyun Shen, and Xiangfeng Wang. "Generalized Knowledge Distillation for Unimodal Glioma Segmentation from Multimodal Models." Electronics 12, no. 7 (March 23, 2023): 1516. http://dx.doi.org/10.3390/electronics12071516.
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Gliomas, primary brain tumors arising from glial cells, can be effectively identified using Magnetic Resonance Imaging (MRI), a widely employed diagnostic tool in clinical settings. Accurate glioma segmentation, which is crucial for diagnosis and surgical intervention, can be achieved by integrating multiple MRI modalities that offer complementary information. However, limited access to multiple modalities in certain clinical contexts often results in suboptimal performance of glioma segmentation methods. This study introduces a novel generalized knowledge distillation framework designed to transfer multimodal knowledge from a teacher model to a unimodal student model via two distinct distillation strategies: segmentation graph distillation and cascade region attention distillation. The former enables the student to replicate the teacher’s softened output, whereas the latter facilitates extraction and learning of region feature information at various levels within the teacher model. Our evaluation of the proposed distillation strategies using the BraTS 2018 dataset confirms their superior performance in unimodal segmentation contexts compared with existing methods.
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Wang, Yu, and Jianping Zhang. "CMMCSegNet: Cross-Modality Multicascade Indirect LGE Segmentation on Multimodal Cardiac MR." Computational and Mathematical Methods in Medicine 2021 (June 5, 2021): 1–14. http://dx.doi.org/10.1155/2021/9942149.
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Since Late-Gadolinium Enhancement (LGE) of cardiac magnetic resonance (CMR) visualizes myocardial infarction, and the balanced-Steady State Free Precession (bSSFP) cine sequence can capture cardiac motions and present clear boundaries; multimodal CMR segmentation has played an important role in the assessment of myocardial viability and clinical diagnosis, while automatic and accurate CMR segmentation still remains challenging due to a very small amount of labeled LGE data and the relatively low contrasts of LGE. The main purpose of our work is to learn the real/fake bSSFP modality with ground truths to indirectly segment the LGE modality of cardiac MR by using a proposed cross-modality multicascade framework: cross-modality translation network and automatic segmentation network, respectively. In the segmentation stage, a novel multicascade pix2pix network is designed to segment the fake bSSFP sequence obtained from a cross-modality translation network. Moreover, we propose perceptual loss measuring features between ground truth and prediction, which are extracted from the pretrained vgg network in the segmentation stage. We evaluate the performance of the proposed method on the multimodal CMR dataset and verify its superiority over other state-of-the-art approaches under different network structures and different types of adversarial losses in terms of dice accuracy in testing. Therefore, the proposed network is promising for Indirect Cardiac LGE Segmentation in clinical applications.
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Yang, Qihong, Ruijun Jing, and Jiliang Mu. "Multi-Modal MR Image Segmentation Strategy for Brain Tumors Based on Domain Adaptation." Computers 13, no. 12 (December 19, 2024): 347. https://doi.org/10.3390/computers13120347.
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During the study of multimodal brain tumor MR image segmentation, the large differences in the image distributions make the assumption that the conditional probabilities are similar when the edge distributions of the target and source domains are similar, and that the edge distributions are similar when the conditional probabilities are similar, not valid. In addition, the training network is usually trained on single domain data, which creates a tendency for the network to represent the image towards the source domain when the target domain is not labeled. Based on the aforementioned reasons, a new multimodal brain tumor MR segmentation strategy based on domain adaptation is proposed in this study. First, the source domain targets for each modality are derived through the clustering methods in the pre-training stage to select the target domain images with the strongest complementarity in the source domain and further produce the pseudo labels. Second, feature adapters are proposed to improve the feature alignment, and a network sensitive to both source and target domain images is designed to comprehensively leverage the multimodal image information. These measures mitigate the domain shift problem and improve the generalization ability of the model, enhancing the accuracy of multimodal brain tumor MR image segmentation.
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Guo, Ruitian, Ruopeng Zhang, Hao Zhou, Tunjun Xie, Yuting Peng, Xili Chen, Guo Yu, et al. "CTDUNet: A Multimodal CNN–Transformer Dual U-Shaped Network with Coordinate Space Attention for Camellia oleifera Pests and Diseases Segmentation in Complex Environments." Plants 13, no. 16 (August 15, 2024): 2274. http://dx.doi.org/10.3390/plants13162274.
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Camellia oleifera is a crop of high economic value, yet it is particularly susceptible to various diseases and pests that significantly reduce its yield and quality. Consequently, the precise segmentation and classification of diseased Camellia leaves are vital for managing pests and diseases effectively. Deep learning exhibits significant advantages in the segmentation of plant diseases and pests, particularly in complex image processing and automated feature extraction. However, when employing single-modal models to segment Camellia oleifera diseases, three critical challenges arise: (A) lesions may closely resemble the colors of the complex background; (B) small sections of diseased leaves overlap; (C) the presence of multiple diseases on a single leaf. These factors considerably hinder segmentation accuracy. A novel multimodal model, CNN–Transformer Dual U-shaped Network (CTDUNet), based on a CNN–Transformer architecture, has been proposed to integrate image and text information. This model first utilizes text data to address the shortcomings of single-modal image features, enhancing its ability to distinguish lesions from environmental characteristics, even under conditions where they closely resemble one another. Additionally, we introduce Coordinate Space Attention (CSA), which focuses on the positional relationships between targets, thereby improving the segmentation of overlapping leaf edges. Furthermore, cross-attention (CA) is employed to align image and text features effectively, preserving local information and enhancing the perception and differentiation of various diseases. The CTDUNet model was evaluated on a self-made multimodal dataset compared against several models, including DeeplabV3+, UNet, PSPNet, Segformer, HrNet, and Language meets Vision Transformer (LViT). The experimental results demonstrate that CTDUNet achieved an mean Intersection over Union (mIoU) of 86.14%, surpassing both multimodal models and the best single-modal model by 3.91% and 5.84%, respectively. Additionally, CTDUNet exhibits high balance in the multi-class segmentation of Camellia oleifera diseases and pests. These results indicate the successful application of fused image and text multimodal information in the segmentation of Camellia disease, achieving outstanding performance.
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Li, Xiang, Junbo Yin, Botian Shi, Yikang Li, Ruigang Yang, and Jianbing Shen. "LWSIS: LiDAR-Guided Weakly Supervised Instance Segmentation for Autonomous Driving." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 1433–41. http://dx.doi.org/10.1609/aaai.v37i2.25228.
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Image instance segmentation is a fundamental research topic in autonomous driving, which is crucial for scene understanding and road safety. Advanced learning-based approaches often rely on the costly 2D mask annotations for training. In this paper, we present a more artful framework, LiDAR-guided Weakly Supervised Instance Segmentation (LWSIS), which leverages the off-the-shelf 3D data, i.e., Point Cloud, together with the 3D boxes, as natural weak supervisions for training the 2D image instance segmentation models. Our LWSIS not only exploits the complementary information in multimodal data during training but also significantly reduces the annotation cost of the dense 2D masks. In detail, LWSIS consists of two crucial modules, Point Label Assignment (PLA) and Graph-based Consistency Regularization (GCR). The former module aims to automatically assign the 3D point cloud as 2D point-wise labels, while the atter further refines the predictions by enforcing geometry and appearance consistency of the multimodal data. Moreover, we conduct a secondary instance segmentation annotation on the nuScenes, named nuInsSeg, to encourage further research on multimodal perception tasks. Extensive experiments on the nuInsSeg, as well as the large-scale Waymo, show that LWSIS can substantially improve existing weakly supervised segmentation models by only involving 3D data during training. Additionally, LWSIS can also be incorporated into 3D object detectors like PointPainting to boost the 3D detection performance for free. The code and dataset are available at https://github.com/Serenos/LWSIS.
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Wan, Yizhou, Roushanak Rahmat, and Stephen J. Price. "Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival." Acta Neurochirurgica 162, no. 12 (July 13, 2020): 3067–80. http://dx.doi.org/10.1007/s00701-020-04483-7.
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Abstract Background Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network–assisted segmentation is correlated with survival. Methods Preoperative MRI of 120 patients were scored using Visually Accessible Rembrandt Images (VASARI) features. We trained and tested a multilayer, multi-scale convolutional neural network on multimodal brain tumour segmentation challenge (BRATS) data, prior to testing on our dataset. The automated labels were manually edited to generate ground truth segmentations. Network performance for our data and BRATS data was compared. Multivariable Cox regression analysis corrected for multiple testing using the false discovery rate was performed to correlate clinical and imaging variables with overall survival. Results Median Dice coefficients in our sample were (1) whole tumour 0.94 (IQR, 0.82–0.98) compared to 0.91 (IQR, 0.83–0.94 p = 0.012), (2) FLAIR region 0.84 (IQR, 0.63–0.95) compared to 0.81 (IQR, 0.69–0.8 p = 0.170), (3) contrast-enhancing region 0.91 (IQR, 0.74–0.98) compared to 0.83 (IQR, 0.78–0.89 p = 0.003) and (4) necrosis region were 0.82 (IQR, 0.47–0.97) compared to 0.67 (IQR, 0.42–0.81 p = 0.005). Contrast-enhancing region/tumour core ratio (HR 4.73 [95% CI, 1.67–13.40], corrected p = 0.017) and necrotic core/tumour core ratio (HR 8.13 [95% CI, 2.06–32.12], corrected p = 0.011) were independently associated with overall survival. Conclusion Semi-automated segmentation of glioblastoma using a convolutional neural network trained on independent data is robust when applied to routine clinical data. The segmented volumes have prognostic significance.
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Fu, Xiaohang, Lei Bi, Ashnil Kumar, Michael Fulham, and Jinman Kim. "Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation." IEEE Journal of Biomedical and Health Informatics 25, no. 9 (September 2021): 3507–16. http://dx.doi.org/10.1109/jbhi.2021.3059453.
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Nandeesh, M. D., and M. Meenakshi. "Detection of Tumor Using Gabor Filter for Multimodal Images." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4325–30. http://dx.doi.org/10.1166/jctn.2020.9070.
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Imaging segmentation techniques play a significant factor in medical justification for diagnosis and therapy application in healthcare industries. These noninvasive procedures assist the physician to visualize the vital part of the human body planned for treatment. Multimodal fused images from Computer tomography (CT) and Magnetic resonance imaging (MRI) provides prominent results in detection of the tumor. Maximum information about the image cannot be obtained from individual technique to assess the location and its dimension of tumor. A fusion of multimodal images like MRI and CT images are used to complimentary information and its segmentation to detect the presence or absence of tumor using objective method. In this paper fusion of CT and MRI is done by a hybrid technique by combining Principal Component Analysis (PCA) and Curvelet Transformation (CVT). Gabor filter based segmentation of this image is applied as post-processing to obtain the presence of exact location of tumor in the image. Performance of fusion and segmentation is analyzed to obtain better quality image. The simulation consequence has shown better images using a hybrid fusion algorithm and Gabor filter is used for assisting the physician to find the presence or absence of tumor. Proposed approach based on simulation results has shown a better efficiency as compared to individual techniques.
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Luo, Tao, and YaLing Li. "Research and Analysis of Brain Glioma Imaging Based on Deep Learning." Journal of Healthcare Engineering 2021 (November 18, 2021): 1–8. http://dx.doi.org/10.1155/2021/3426080.
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The incidence of glioma is increasing year by year, seriously endangering people’s health. Magnetic resonance imaging (MRI) can effectively provide intracranial images of brain tumors and provide strong support for the diagnosis and treatment of the disease. Accurate segmentation of brain glioma has positive significance in medicine. However, due to the strong variability of the size, shape, and location of glioma and the large differences between different cases, the recognition and segmentation of glioma images are very difficult. Traditional methods are time-consuming, labor-intensive, and inefficient, and single-modal MRI images cannot provide comprehensive information about gliomas. Therefore, it is necessary to synthesize multimodal MRI images to identify and segment glioma MRI images. This work is based on multimodal MRI images and based on deep learning technology to achieve automatic and efficient segmentation of gliomas. The main tasks are as follows. A deep learning model based on dense blocks of holes, 3D U-Net, is proposed. It can automatically segment multimodal MRI glioma images. U-Net network is often used in image segmentation and has good performance. However, due to the strong specificity of glioma, the U-Net model cannot effectively obtain more details. Therefore, the 3D U-Net model proposed in this paper can integrate hollow convolution and densely connected blocks. In addition, this paper also combines classification loss and cross-entropy loss as the loss function of the network to improve the problem of category imbalance in glioma image segmentation tasks. The algorithm proposed in this paper has been used to perform a lot of experiments on the BraTS2018 dataset, and the results prove that this model has good segmentation performance.
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Wu, Xiaoqin, Xiaoli Yang, Zhenwei Li, Lipei Liu, and Yuxin Xia. "Multimodal brain tumor image segmentation based on DenseNet." PLOS ONE 19, no. 1 (January 18, 2024): e0286125. http://dx.doi.org/10.1371/journal.pone.0286125.
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A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient’s condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.
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Li, Jun, Ting Zhang, Yi Zhao, Nan Chen, Han Zhou, Hongtao Xu, Zihao Guan, et al. "MC-UNet: Multimodule Concatenation Based on U-Shape Network for Retinal Blood Vessels Segmentation." Computational Intelligence and Neuroscience 2022 (November 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/9917691.
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Accurate retinal blood vessels segmentation is an important step in the clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make blood vessel segmentation still very challenging. This paper proposes a novel multimodule concatenation via a U-shaped network for retinal vessels segmentation, which is based on atrous convolution and multikernel pooling. The proposed network structure retains three layers of the essential structure of U-Net, in which the atrous convolution combining the multikernel pooling blocks are designed to obtain more contextual information. The spatial attention module is concatenated with the dense atrous convolution module and the multikernel pooling module to form a multimodule concatenation. And different dilation rates are selected by cascading to acquire a larger receptive field in atrous convolution. Adequate comparative experiments are conducted on these public retinal datasets: DRIVE, STARE, and CHASE_DB1. The results show that the proposed method is effective, especially for microvessels. The code will be released at https://github.com/rocklijun/MC-UNet.
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V, Slyusar. "The text segmentation by neural networks of image segmentation." Artificial Intelligence 29, AI.2024.29(1) (March 20, 2024): 46–55. http://dx.doi.org/10.15407/jai2024.01.046.
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The article highlights the importance of text segmentation in the field of natural language processing (NLP), especially in light of the development of large language models such as GPT-4. It discusses the use of specialized segmentation neural networks for various tasks, such as processing passport data and other documents, and points out the possibility of integrating these technologies into mobile applications. The use of neural network architectures, geared towards image processing, for text segmentation is considered. The study describes the application of networks such as PSPNet, U-Net, and U-Net++ for processing textual data, with an emphasis on adapting these networks to text tasks and evaluating their effectiveness. The potential of the multimodal capabilities of modern neural networks and the need for further research in this field are emphasized.
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Zhao, Liya, and Kebin Jia. "Multiscale CNNs for Brain Tumor Segmentation and Diagnosis." Computational and Mathematical Methods in Medicine 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/8356294.
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Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.
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Sumathi, R., and M. Venkatesulu. "An Automated Hybrid Approach for Multimodal Tumor Segmentation." Journal of Physics: Conference Series 1979, no. 1 (August 1, 2021): 012047. http://dx.doi.org/10.1088/1742-6596/1979/1/012047.
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Chen, Cheng, Qi Dou, Yueming Jin, Quande Liu, and Pheng Ann Heng. "Learning With Privileged Multimodal Knowledge for Unimodal Segmentation." IEEE Transactions on Medical Imaging 41, no. 3 (March 2022): 621–32. http://dx.doi.org/10.1109/tmi.2021.3119385.
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Ali, A. M., N. Alajlan, A. A. Farag, and A. A. Farag. "Multimodal imaging: modelling and segmentation with biomedical applications." IET Computer Vision 6, no. 6 (November 1, 2012): 524–39. http://dx.doi.org/10.1049/iet-cvi.2010.0125.
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Viergever, M. A., J. B. A. Maintz, W. J. Niessen, H. J. Noordmans, J. P. W. Pluim, R. Stokking, and K. L. Vincken. "Registration, segmentation, and visualization of multimodal brain images." Computerized Medical Imaging and Graphics 25, no. 2 (March 2001): 147–51. http://dx.doi.org/10.1016/s0895-6111(00)00065-3.
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Bouyahi, Mohamed, and Yassine Ben Ayed. "Video Scenes Segmentation Based on Multimodal Genre Prediction." Procedia Computer Science 176 (2020): 10–21. http://dx.doi.org/10.1016/j.procs.2020.08.002.
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