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Статті в журналах з теми "Segmentation models"

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Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Nishiyama, Daisuke, Hiroshi Iwasaki, Takaya Taniguchi, Daisuke Fukui, Manabu Yamanaka, Teiji Harada, and Hiroshi Yamada. "Deep generative models for automated muscle segmentation in computed tomography scanning." PLOS ONE 16, no. 9 (September 10, 2021): e0257371. http://dx.doi.org/10.1371/journal.pone.0257371.

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Accurate gluteus medius (GMd) volume evaluation may aid in the analysis of muscular atrophy states and help gain an improved understanding of patient recovery via rehabilitation. However, the segmentation of muscle regions in GMd images for cubic muscle volume assessment is time-consuming and labor-intensive. This study automated GMd-region segmentation from the computed tomography (CT) images of patients diagnosed with hip osteoarthritis using deep learning and evaluated the segmentation accuracy. To this end, 5250 augmented pairs of training data were obtained from five participants, and a conditional generative adversarial network was used to identify the relationships between the image pairs. Using the preserved test datasets, the results of automatic segmentation with the trained deep learning model were compared to those of manual segmentation in terms of the dice similarity coefficient (DSC), volume similarity (VS), and shape similarity (MS). As observed, the average DSC values for automatic and manual segmentations were 0.748 and 0.812, respectively, with a significant difference (p < 0.0001); the average VS values were 0.247 and 0.203, respectively, with no significant difference (p = 0.069); and the average MS values were 1.394 and 1.156, respectively, with no significant difference (p = 0.308). The GMd volumes obtained by automatic and manual segmentation were 246.2 cm3 and 282.9 cm3, respectively. The noninferiority of the DSC obtained by automatic segmentation was verified against that obtained by manual segmentation. Accordingly, the proposed GAN-based automatic GMd-segmentation technique is confirmed to be noninferior to manual segmentation. Therefore, the findings of this research confirm that the proposed method not only reduces time and effort but also facilitates accurate assessment of the cubic muscle volume.

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2

Iyer, Aditi, Maria Thor, Ifeanyirochukwu Onochie, Jennifer Hesse, Kaveh Zakeri, Eve LoCastro, Jue Jiang, et al. "Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT." Physics in Medicine & Biology 67, no. 2 (January 17, 2022): 024001. http://dx.doi.org/10.1088/1361-6560/ac4000.

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Abstract Objective. Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process. Approach. CT scans of 242 head and neck (H&N) cancer patients acquired from 2004 to 2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded framework was used, wherein models were trained sequentially to spatially constrain each structure group based on prior segmentations. Additionally, an ensemble of models, combining contextual information from axial, coronal, and sagittal views was used to improve segmentation accuracy. Prospective evaluation was conducted by measuring the amount of manual editing required in 91 H&N CT scans acquired February-May 2021. Main results. Medians and inter-quartile ranges of Dice similarity coefficients (DSC) computed on the retrospective testing set (N = 24) were 0.87 (0.85–0.89) for the masseters, 0.80 (0.79–0.81) for the medial pterygoids, 0.81 (0.79–0.84) for the larynx, and 0.69 (0.67–0.71) for the constrictor. Auto-segmentations, when compared to two sets of manual segmentations in 10 randomly selected scans, showed better agreement (DSC) with each observer than inter-observer DSC. Prospective analysis showed most manual modifications needed for clinical use were minor, suggesting auto-contouring could increase clinical efficiency. Trained segmentation models are available for research use upon request via https://github.com/cerr/CERR/wiki/Auto-Segmentation-models. Significance. We developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT and demonstrated its potential for use in treatment planning to limit complications post-RT. To the best of our knowledge, this is the only prospectively-validated deep learning-based model for segmenting chewing and swallowing structures in CT. Segmentation models have been made open-source to facilitate reproducibility and multi-institutional research.

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3

van der Putten, Joost, Fons van der Sommen, Jeroen de Groof, Maarten Struyvenberg, Svitlana Zinger, Wouter Curvers, Erik Schoon, Jacques Bergman, and Peter H. N. de With. "Modeling clinical assessor intervariability using deep hypersphere encoder–decoder networks." Neural Computing and Applications 32, no. 14 (November 21, 2019): 10705–17. http://dx.doi.org/10.1007/s00521-019-04607-w.

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AbstractIn medical imaging, a proper gold-standard ground truth as, e.g., annotated segmentations by assessors or experts is lacking or only scarcely available and suffers from large intervariability in those segmentations. Most state-of-the-art segmentation models do not take inter-observer variability into account and are fully deterministic in nature. In this work, we propose hypersphere encoder–decoder networks in combination with dynamic leaky ReLUs, as a new method to explicitly incorporate inter-observer variability into a segmentation model. With this model, we can then generate multiple proposals based on the inter-observer agreement. As a result, the output segmentations of the proposed model can be tuned to typical margins inherent to the ambiguity in the data. For experimental validation, we provide a proof of concept on a toy data set as well as show improved segmentation results on two medical data sets. The proposed method has several advantages over current state-of-the-art segmentation models such as interpretability in the uncertainty of segmentation borders. Experiments with a medical localization problem show that it offers improved biopsy localizations, which are on average 12% closer to the optimal biopsy location.

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4

Olson, Richard K., and Janice M. Keenan. "Segmentation in models of reading." Behavioral and Brain Sciences 8, no. 4 (December 1985): 719–20. http://dx.doi.org/10.1017/s0140525x00045866.

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5

Gwadera, Robert, Aristides Gionis, and Heikki Mannila. "Optimal segmentation using tree models." Knowledge and Information Systems 15, no. 3 (July 28, 2007): 259–83. http://dx.doi.org/10.1007/s10115-007-0091-5.

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6

Chou, Glen, Necmiye Ozay, and Dmitry Berenson. "Incremental Segmentation of ARX Models." IFAC-PapersOnLine 51, no. 15 (2018): 587–92. http://dx.doi.org/10.1016/j.ifacol.2018.09.222.

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7

Golovinskiy, Aleksey, and Thomas Funkhouser. "Consistent segmentation of 3D models." Computers & Graphics 33, no. 3 (June 2009): 262–69. http://dx.doi.org/10.1016/j.cag.2009.03.010.

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8

SIMMONS, D. "Categorical models of image segmentation." Ophthalmic and Physiological Optics 11, no. 3 (July 1991): 282. http://dx.doi.org/10.1016/0275-5408(91)90113-w.

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9

Mohd Ghani, Noor Ain Syazwani, and Abdul Kadir Jumaat. "Selective Segmentation Model for Vector-Valued Images." Journal of Information and Communication Technology 21, No.2 (April 7, 2022): 149–73. http://dx.doi.org/10.32890/jict2022.21.2.1.

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One of the most important steps in image processing and computer vision for image analysis is segmentation, which can be classified into global and selective segmentations. Global segmentation models can segment whole objects in an image. Unfortunately, these models are unable to segment a specific object that is required for extraction. To overcome this limitation, the selective segmentation model, which is capable of extracting a particular object or region in an image, must be prioritised. Recent selective segmentation models have shown to be effective in segmenting greyscale images. Nevertheless, if the input is vector-valued or identified as a colour image, the models simply ignore the colour information by converting that image into a greyscale format. Colour plays an important role in the interpretation of object boundaries within an image as it helps to provide a more detailed explanation of the scene’s objects. Therefore, in this research, a model for selective segmentation of vector-valued images is proposed by combining concepts from existing models. The finite difference method was used to solve the resulting Euler-Lagrange (EL) partial differential equation of the proposed model. The accuracy of the proposed model’s segmentation output was then assessed using visual observation as well as by using two similarity indices, namely the Jaccard (JSC) and Dice (DSC) similarity coefficients. Experimental results demonstrated that the proposed model is capable of successfully segmenting a specific object in vector-valued images. Future research on this area can be further extended in three-dimensional modelling.

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10

Tseng, Din-Chang, and Ruei-Lung Chen. "Mutiscale Texture Segmentation Using Contextual Hidden Markov Tree Models." International Journal of Machine Learning and Computing 5, no. 3 (June 2015): 198–205. http://dx.doi.org/10.7763/ijmlc.2015.v5.507.

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11

Weishaupt, L. L., T. Vuong, A. Thibodeau-Antonacci, A. Garant, K. S. Singh, C. Miller, A. Martin, and S. Enger. "A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING." Journal of the Canadian Association of Gastroenterology 5, Supplement_1 (February 21, 2022): 140–42. http://dx.doi.org/10.1093/jcag/gwab049.120.

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Abstract Background Tumor delineation in endoscopy images is a crucial part of clinical diagnoses and treatment planning for rectal cancer patients. However, it is challenging to detect and adequately determine the size of tumors in these images, especially for inexperienced clinicians. This motivates the need for a standardized, automated segmentation method. While deep learning has proven to be a powerful tool for medical image segmentation, it requires a large quantity of high-quality annotated training data. Since the annotation of endoscopy images is prone to high inter-observer variability, creating a robust unbiased deep learning model for this task is challenging. Aims To quantify the inter-observer variability in the manual segmentation of tumors in endoscopy images of rectal cancer patients and investigate an automated approach using deep learning. Methods Three gastrointestinal physicians and radiation oncologists (G1, G2, and G3) segmented 2833 endoscopy images into tumor and non-tumor regions. The whole image classifications and the pixelwise classifications into tumor and non-tumor were compared to quantify the inter-observer variability. Each manual annotator is from a different institution. Three different deep learning architectures (FCN32, U-Net, and SegNet) were trained on the binary contours created by G2. This naive approach investigates the effectiveness of neglecting any information about the uncertainty associated with the task of tumor delineation. Finally, segmentations from G2 and the deep learning models’ predictions were compared against ground truth labels from G1 and G3, and accuracy, sensitivity, specificity, precision, and F1 scores were computed for images where both segmentations contained tumors. Results The deep-learning segmentation took less than 1 second, while manual segmentation took approximately 10 seconds per image. There was significant inter-observer variability for the whole-image classifications made by the manual annotators (Figure 1A). The segmentation scores achieved by the deep learning models (SegNet F1:0.80±0.08) were comparable to the inter-observer variability for the pixel-wise image classification (Figure 1B). Conclusions The large inter-observer variability observed in this study indicates a need for an automated segmentation tool for tumors in endoscopy images of rectal cancer patients. While deep learning models trained on a single observer’s labels can segment tumors with an accuracy similar to the inter-observer variability, these models do not accurately reflect the intrinsic uncertainty associated with tumor delineation. In our ongoing studies, we investigate training a model with all observers’ contours to reflect the uncertainty associated with the tumor segmentations. Funding Agencies CIHRNSERC

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12

Lewandowicz, Elżbieta, and Przemysław Lisowski. "METHODOLOGY TO GENERATE NAVIGATION MODELS IN BUILDING." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 24, no. 8 (December 14, 2018): 619–29. http://dx.doi.org/10.3846/jcem.2018.6599.

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Indoor route networks models are created for use in navigation. They may be built manually, but it is better to generate them automatically, based on the building floor plans. Research has been conducted in this field in many research centers. The authors undertook to develop their own methodology for generating navigation networks, using topological neighborhood relations and semantic data. The research project focuses on one floor in a building, which consists of rooms and an expanded corridor with an obstacle in the form of an open space between the floors. The first stage of the project consisted in the segmentation of the corridor space to improve its resolution. The objective of the conducted research was to select special points (five suggestions) for the segmentation. As a result, five different segmentations of the corridor space were obtained. The aim of the second stage was to automatically generate five navigation network models. The graphically presented results have been verified against the routes generated between the selected points in the building plan. A comparison of the results with other solutions shows that the routes generated in the presented methodology are more straight-line and less zigzagging.

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13

Shu, Zhen Yu, Guo Zhao Wang, and Liang Zhong Fan. "Fast Mesh Segmentation for CAD Models." Key Engineering Materials 460-461 (January 2011): 780–85. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.780.

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In this paper, we present a fast method for segmentation of triangular meshes into simple patches. The method is suitable for commonly used CAD models. Given a mesh surface, all faces of it cluster to a user-specified number of patches according to similarity of curvatures. Experimental results show that our algorithm is very efficient and effective.

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14

Pourquié, Olivier, and Albert Goldbeter. "Segmentation clock: insights from computational models." Current Biology 13, no. 16 (August 2003): R632—R634. http://dx.doi.org/10.1016/s0960-9822(03)00567-0.

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15

Rougon, Nicolas. "Directional adaptive deformable models for segmentation." Journal of Electronic Imaging 7, no. 1 (January 1, 1998): 231. http://dx.doi.org/10.1117/1.482641.

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16

Hammal, Z., N. Eveno, A. Caplier, and Py Coulon. "Parametric models for facial features segmentation." Signal Processing 86, no. 2 (February 2006): 399–413. http://dx.doi.org/10.1016/j.sigpro.2005.06.006.

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17

Penny, William D., and Stephen J. Roberts. "Dynamic Models for Nonstationary Signal Segmentation." Computers and Biomedical Research 32, no. 6 (December 1999): 483–502. http://dx.doi.org/10.1006/cbmr.1999.1511.

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18

Florez-Lopez, Raquel, and Juan Manuel Ramon-Jeronimo. "Marketing Segmentation Through Machine Learning Models." Social Science Computer Review 27, no. 1 (April 7, 2008): 96–117. http://dx.doi.org/10.1177/0894439308321592.

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19

Yi Yang, S. Hallman, D. Ramanan, and C. C. Fowlkes. "Layered Object Models for Image Segmentation." IEEE Transactions on Pattern Analysis and Machine Intelligence 34, no. 9 (September 2012): 1731–43. http://dx.doi.org/10.1109/tpami.2011.208.

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20

PUJOL, ORIOL, and PETIA RADEVA. "TEXTURE SEGMENTATION BY STATISTICAL DEFORMABLE MODELS." International Journal of Image and Graphics 04, no. 03 (July 2004): 433–52. http://dx.doi.org/10.1142/s021946780400149x.

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Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model.

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21

Manfredi, Marco, Costantino Grana, Rita Cucchiara, and Arnold W. M. Smeulders. "Segmentation models diversity for object proposals." Computer Vision and Image Understanding 158 (May 2017): 40–48. http://dx.doi.org/10.1016/j.cviu.2016.06.005.

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22

Bassi, Francesca. "Dynamic segmentation with growth mixture models." Advances in Data Analysis and Classification 10, no. 2 (January 13, 2016): 263–79. http://dx.doi.org/10.1007/s11634-015-0230-x.

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23

Kang, Jaeyong, and Jeonghwan Gwak. "Ensemble of Instance Segmentation Models for Polyp Segmentation in Colonoscopy Images." IEEE Access 7 (2019): 26440–47. http://dx.doi.org/10.1109/access.2019.2900672.

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Kim, Yong-Woon, Yung-Cheol Byun, and Addapalli V. N. Krishna. "Portrait Segmentation Using Ensemble of Heterogeneous Deep-Learning Models." Entropy 23, no. 2 (February 5, 2021): 197. http://dx.doi.org/10.3390/e23020197.

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Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmentation, which is a subset of semantic image segmentation, is widely used as a preprocessing step in multiple applications such as security systems, entertainment applications, video conferences, etc. A substantial amount of deep learning-based portrait segmentation approaches have been developed, since the performance and accuracy of semantic image segmentation have improved significantly due to the recent introduction of deep learning technology. However, these approaches are limited to a single portrait segmentation model. In this paper, we propose a novel approach using an ensemble method by combining multiple heterogeneous deep-learning based portrait segmentation models to improve the segmentation performance. The Two-Models ensemble and Three-Models ensemble, using a simple soft voting method and weighted soft voting method, were experimented. Intersection over Union (IoU) metric, IoU standard deviation and false prediction rate were used to evaluate the performance. Cost efficiency was calculated to analyze the efficiency of segmentation. The experiment results show that the proposed ensemble approach can perform with higher accuracy and lower errors than single deep-learning-based portrait segmentation models. The results also show that the ensemble of deep-learning models typically increases the use of memory and computing power, although it also shows that the ensemble of deep-learning models can perform more efficiently than a single model with higher accuracy using less memory and less computing power.

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Hartmann, Martín, Olivier Lartillot, and Petri Toiviainen. "Multi-scale Modelling of Segmentation." Music Perception 34, no. 2 (December 1, 2016): 192–217. http://dx.doi.org/10.1525/mp.2016.34.2.192.

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While listening to music, people often unwittingly break down musical pieces into constituent chunks such as verses and choruses. Music segmentation studies have suggested that some consensus regarding boundary perception exists, despite individual differences. However, neither the effects of experimental task (i.e., real-time vs. annotated segmentation), nor of musicianship on boundary perception are clear. Our study assesses musicianship effects and differences between segmentation tasks. We conducted a real-time experiment to collect segmentations by musicians and nonmusicians from nine musical pieces. In a second experiment on non-real-time segmentation, musicians indicated boundaries and their strength for six examples. Kernel density estimation was used to develop multi-scale segmentation models. Contrary to previous research, no relationship was found between boundary strength and boundary indication density, although this might be contingent on stimuli and other factors. In line with other studies, no musicianship effects were found: our results showed high agreement between groups and similar inter-subject correlations. Also consistent with previous work, time scales between one and two seconds were optimal for combining boundary indications. In addition, we found effects of task on number of indications, and a time lag between tasks dependent on beat length. Also, the optimal time scale for combining responses increased when the pulse clarity or event density decreased. Implications for future segmentation studies are raised concerning the selection of time scales for modelling boundary density, and time alignment between models.

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Lin, Yih-Lon, Adam Huang, Chung-Yi Yang, and Wen-Yu Chang. "Measurement of Body Surface Area for Psoriasis Using U-net Models." Computational and Mathematical Methods in Medicine 2022 (February 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/7960151.

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During the evaluation of body surface area (BSA), precise measurement of psoriasis is crucial for assessing disease severity and modulating treatment strategies. Physicians usually evaluate patients subjectively through direct visual evaluation. However, judgment based on the naked eye is not reliable. This study is aimed at evaluating the use of machine learning methods, specifically U-net models, and developing an artificial neural network prediction model for automated psoriasis lesion segmentation and BSA measurement. The segmentation of psoriasis lesions using deep learning is adopted to measure the BSA of psoriasis so that the severity can be evaluated automatically in patients. An automated psoriasis lesion segmentation method based on the U-net architecture was used with a focus on high-resolution images and estimation of the BSA. The proposed method trained the model with the same patch size of 512 × 512 and predicted testing images with different patch sizes. We collected 255 high-resolution psoriasis images representing large anatomical sites, such as the trunk and extremities. The average residual of the ground truth image and the predicted image was approximately 0.033. The interclass correlation coefficient between the U-net and dermatologist’s segmentations measured in the ratio of affected psoriasis over the body area in the test dataset was 0.966 (95% CI: 0.981–0.937), indicating strong agreement. Herein, the proposed U-net model achieved dermatologist-level performance in estimating the involved BSA for psoriasis.

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Meziane, Abdelfettah, Saïd MAHMOUDI, and Mohammed Amine CHIKH. "Brain Structures Segmentation by using Statistical Models." Medical Technologies Journal 1, no. 3 (September 28, 2017): 59. http://dx.doi.org/10.26415/2572-004x-vol1iss3p59-59.

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Automatic segmentation of brain structures is a fundamental step for quantitative analysis of images in many brain’s pathologies such as Alzheimer’s, brain’s tumors or multiple sclerosis. The large variation of brain structures requires the development of efficient and specific methods, often by using Magnetic Resonance Imaging (MRI) modality. The goal of our work is to implement an automatic brain’s structures segmentation method that uses the active shape models (ASM) and active appearance models (AAM) techniques. Another goal of this work is to compare the performances of these segmentation approaches, and also to evaluate their use in a computer aided diagnosis tools and to compare their performances.

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Sapin, Alexander Sergeevich. "Building neural network models for morphological and morpheme analysis of texts." Proceedings of the Institute for System Programming of the RAS 33, no. 4 (2021): 117–30. http://dx.doi.org/10.15514/ispras-2021-33(4)-9.

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Morphological analysis of text is one of the most important stages of natural language processing (NLP). Traditional and well-studied problems of morphological analysis include normalization (lemmatization) of a given word form, recognition of its morphological characteristics and their morphological disambiguation. The morphological analysis also involves the problem of morpheme segmentation of words (i.e., segmentation of words into constituent morphs and their classification), which is actual in some NLP applications. In recent years, several machine learning models have been developed, which increase the accuracy of traditional morphological analysis and morpheme segmentation, but performance of such models is insufficient for many applied problems. For morpheme segmentation, high-precision models have been built only for lemmas (normalized word forms). This paper describes two new high-accuracy neural network models that implement morphemic segmentation of Russian word forms with sufficiently high performance. The first model is based on convolutional neural networks and shows the state-of-the-art quality of morphemic segmentation for Russian word forms. The second model, besides morpheme segmentation of a word form, preliminarily refines its morphological characteristics, thereby performing their disambiguation. The performance of this joined morphological model is the best among the considered morpheme segmentation models, with comparable accuracy of segmentation.

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Wang, Guodong, Jie Xu, Qian Dong, and Zhenkuan Pan. "Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation." International Journal of Biomedical Imaging 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/237648.

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Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities.

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Yang, Zi, Mingli Chen, Mahdieh Kazemimoghadam, Lin Ma, Strahinja Stojadinovic, Robert Timmerman, Tu Dan, Zabi Wardak, Weiguo Lu, and Xuejun Gu. "Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation." Physics in Medicine & Biology 67, no. 2 (January 19, 2022): 025004. http://dx.doi.org/10.1088/1361-6560/ac4667.

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Abstract Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could become a pronounced time bottleneck. Our group has developed an automated BMs segmentation platform to assist in this process. The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive segmentations, mainly caused by the injected contrast during MRI acquisition. To address this problem and further improve the segmentation performance, a deep-learning and radiomics ensemble classifier was developed to reduce the false-positive rate in segmentations. The proposed model consists of a Siamese network and a radiomic-based support vector machine (SVM) classifier. The 2D-based Siamese network contains a pair of parallel feature extractors with shared weights followed by a single classifier. This architecture is designed to identify the inter-class difference. On the other hand, the SVM model takes the radiomic features extracted from 3D segmentation volumes as the input for twofold classification, either a false-positive segmentation or a true BM. Lastly, the outputs from both models create an ensemble to generate the final label. The performance of the proposed model in the segmented mBMs testing dataset reached the accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under the curve of 0.91, 0.96, 0.90 and 0.93, respectively. After integrating the proposed model into the original segmentation platform, the average segmentation false negative rate (FNR) and the false positive over the union (FPoU) were 0.13 and 0.09, respectively, which preserved the initial FNR (0.07) and significantly improved the FPoU (0.55). The proposed method effectively reduced the false-positive rate in the BMs raw segmentations indicating that the integration of the proposed ensemble classifier into the BMs segmentation platform provides a beneficial tool for mBMs SRS management.

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31

Gonçalves, Rita de Cassia Braga, and Sergio Miranda Freire. "Name segmentation using hidden Markov models and its application in record linkage." Cadernos de Saúde Pública 30, no. 10 (October 2014): 2039–48. http://dx.doi.org/10.1590/0102-311x00191313.

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This study aimed to evaluate the use of hidden Markov models (HMM) for the segmentation of person names and its influence on record linkage. A HMM was applied to the segmentation of patient’s and mother’s names in the databases of the Mortality Information System (SIM), Information Subsystem for High Complexity Procedures (APAC), and Hospital Information System (AIH). A sample of 200 patients from each database was segmented via HMM, and the results were compared to those from segmentation by the authors. The APAC-SIM and APAC-AIH databases were linked using three different segmentation strategies, one of which used HMM. Conformity of segmentation via HMM varied from 90.5% to 92.5%. The different segmentation strategies yielded similar results in the record linkage process. This study suggests that segmentation of Brazilian names via HMM is no more effective than traditional segmentation approaches in the linkage process.

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32

Affane, Abir, Adrian Kucharski, Paul Chapuis, Samuel Freydier, Marie-Ange Lebre, Antoine Vacavant, and Anna Fabijańska. "Segmentation of Liver Anatomy by Combining 3D U-Net Approaches." Applied Sciences 11, no. 11 (May 26, 2021): 4895. http://dx.doi.org/10.3390/app11114895.

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Accurate liver vessel segmentation is of crucial importance for the clinical diagnosis and treatment of many hepatic diseases. Recent state-of-the-art methods for liver vessel reconstruction mostly utilize deep learning methods, namely, the U-Net model and its variants. However, to the best of our knowledge, no comparative evaluation has been proposed to compare these approaches in the liver vessel segmentation task. Moreover, most research works do not consider the liver volume segmentation as a preprocessing step, in order to keep only inner hepatic vessels, for Couinaud representation for instance. For these reasons, in this work, we propose using accurate Dense U-Net liver segmentation and conducting a comparison between 3D U-Net models inside the obtained volumes. More precisely, 3D U-Net, Dense U-Net, and MultiRes U-Net are pitted against each other in the vessel segmentation task on the IRCAD dataset. For each model, three alternative setups that allow adapting the selected CNN architectures to volumetric data are tested, namely, full 3D, slab-based, and box-based setups are considered. The results showed that the most accurate setup is the full 3D process, providing the highest Dice for most of the considered models. However, concerning the particular models, the slab-based MultiRes U-Net provided the best score. With our accurate vessel segmentations, several medical applications can be investigated, such as automatic and personalized Couinaud zoning of the liver.

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33

Olveres, Jimena, Erik Carbajal-Degante, Boris Escalante-Ramírez, Enrique Vallejo, and Carla María García-Moreno. "Deformable Models for Segmentation Based on Local Analysis." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/1646720.

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Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

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Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, M. G. Garcia-Hernandez, M. Torres-Cisneros, H. J. Estrada-Garcia, and A. Hernandez-Aguirre. "Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/419018.

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This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

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Hassouna, M. Sabry, A. A. Farag, Stephen Hushek, and Thomas Moriarty. "Cerebrovascular segmentation from TOF using stochastic models." Medical Image Analysis 10, no. 1 (February 2006): 2–18. http://dx.doi.org/10.1016/j.media.2004.11.009.

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36

He, Chen, and Chunmeng Wang. "A Survey on Segmentation of 3D Models." Wireless Personal Communications 102, no. 4 (February 5, 2018): 3835–42. http://dx.doi.org/10.1007/s11277-018-5414-1.

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37

Sivakumar, S., and C. Chandrasekar. "Lung Nodule Segmentation through Unsupervised Clustering Models." Procedia Engineering 38 (2012): 3064–73. http://dx.doi.org/10.1016/j.proeng.2012.06.357.

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38

Beale, Daniel, Pejman Iravani, and Peter Hall. "Probabilistic models for robot-based object segmentation." Robotics and Autonomous Systems 59, no. 12 (December 2011): 1080–89. http://dx.doi.org/10.1016/j.robot.2011.08.003.

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39

Sang, Nong. "Textured image segmentation based on modulation models." Optical Engineering 49, no. 9 (September 1, 2010): 097009. http://dx.doi.org/10.1117/1.3487747.

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40

Seyedhosseini, Mojtaba, and Tolga Tasdizen. "Semantic Image Segmentation with Contextual Hierarchical Models." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 5 (May 1, 2016): 951–64. http://dx.doi.org/10.1109/tpami.2015.2473846.

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41

Andereck, Kathleen L., and Linda L. Caldwell. "Variable Selection in Tourism Market Segmentation Models." Journal of Travel Research 33, no. 2 (October 1994): 40–46. http://dx.doi.org/10.1177/004728759403300207.

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42

Mingolla, Ennio. "Neural models of motion integration and segmentation." Neural Networks 16, no. 5-6 (June 2003): 939–45. http://dx.doi.org/10.1016/s0893-6080(03)00099-6.

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43

WOODCOCK, C., and V. J. HARWARD. "Nested-hierarchical scene models and image segmentation." International Journal of Remote Sensing 13, no. 16 (November 1992): 3167–87. http://dx.doi.org/10.1080/01431169208904109.

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44

Kumar, Yokesh, Ravi Janardan, Brent Larson, and Joe Moon. "Improved Segmentation of Teeth in Dental Models." Computer-Aided Design and Applications 8, no. 2 (January 2011): 211–24. http://dx.doi.org/10.3722/cadaps.2011.211-224.

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45

Koster, André S. E., Koen L. Vincken, Cornelis N. de Graaf, Olaf C. Zander, and Max A. Viergever. "Heuristic Linking Models in Multiscale Image Segmentation." Computer Vision and Image Understanding 65, no. 3 (March 1997): 382–402. http://dx.doi.org/10.1006/cviu.1996.0490.

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46

Sorrentino, R. "Planar Circuits, Waveguide Models, and Segmentation Method." IEEE Transactions on Microwave Theory and Techniques 33, no. 10 (October 1985): 1057–66. http://dx.doi.org/10.1109/tmtt.1985.1133169.

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47

Yang Wang, K. F. Loe, T. Tan, and Jian-Kang Wu. "Spatiotemporal video segmentation based on graphical models." IEEE Transactions on Image Processing 14, no. 7 (July 2005): 937–47. http://dx.doi.org/10.1109/tip.2005.849330.

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48

Borreguero, Margarita, Vahram Atayan, and Sybille Grosse. "Models of discourse segmentation in Romance Languages1." Revue Romane / Langue et littérature. International Journal of Romance Languages and Literatures 53, no. 1 (August 10, 2018): 1–5. http://dx.doi.org/10.1075/rro.00003.int.

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49

Tian Shen, Hongsheng Li, and Xiaolei Huang. "Active Volume Models for Medical Image Segmentation." IEEE Transactions on Medical Imaging 30, no. 3 (March 2011): 774–91. http://dx.doi.org/10.1109/tmi.2010.2094623.

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50

Thiagarajan, Jayaraman J., Karthikeyan Natesan Ramamurthy, Deepta Rajan, Andreas Spanias, Anup Puri, and David Frakes. "Kernel Sparse Models for Automated Tumor Segmentation." International Journal on Artificial Intelligence Tools 23, no. 03 (May 28, 2014): 1460004. http://dx.doi.org/10.1142/s0218213014600045.

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In this paper, we propose sparse coding-based approaches for segmentation of tumor regions from magnetic resonance (MR) images. Sparse coding with data-adapted dictionaries has been successfully employed in several image recovery and vision problems. The proposed approaches obtain sparse codes for each pixel in brain MR images considering their intensity values and location information. Since it is trivial to obtain pixel-wise sparse codes, and combining multiple features in the sparse coding setup is not straight-forward, we propose to perform sparse coding in a high-dimensional feature space where non-linear similarities can be effectively modeled. We use the training data from expert-segmented images to obtain kernel dictionaries with the kernel K-lines clustering procedure. For a test image, sparse codes are computed with these kernel dictionaries, and they are used to identify the tumor regions. This approach is completely automated, and does not require user intervention to initialize the tumor regions in a test image. Furthermore, a low complexity segmentation approach based on kernel sparse codes, which allows the user to initialize the tumor region, is also presented. Results obtained with both the proposed approaches are validated against manual segmentation by an expert radiologist, and it is shown that proposed methods lead to accurate tumor identification.

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