Bibliographies thématiques / Lesions segmentation

Littérature scientifique sur le sujet « Lesions segmentation »

Auteur : Grafiati
Publié le 10 mars 2023

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Articles de revues sur le sujet "Lesions segmentation"

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Ma, Tian, Xinlei Zhou, Jiayi Yang, Boyang Meng, Jiali Qian, Jiehui Zhang et Gang Ge. « Dental Lesion Segmentation Using an Improved ICNet Network with Attention ». Micromachines 13, no 11 (7 novembre 2022) : 1920. http://dx.doi.org/10.3390/mi13111920.

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Precise segmentation of tooth lesions is critical to creation of an intelligent tooth lesion detection system. As a solution to the problem that tooth lesions are similar to normal tooth tissues and difficult to segment, an improved segmentation method of the image cascade network (ICNet) network is proposed to segment various lesion types, such as calculus, gingivitis, and tartar. First, the ICNet network model is used to achieve real-time segmentation of lesions. Second, the Convolutional Block Attention Module (CBAM) is integrated into the ICNet network structure, and large-size convolutions in the spatial attention module are replaced with layered dilated convolutions to enhance the relevant features while suppressing useless features and solve the problem of inaccurate lesion segmentations. Finally, part of the convolution in the network model is replaced with an asymmetric convolution to reduce the calculations added by the attention module. Experimental results show that compared with Fully Convolutional Networks (FCN), U-Net, SegNet, and other segmentation algorithms, our method has a significant improvement in the segmentation effect, and the image processing frequency is higher, which satisfies the real-time requirements of tooth lesion segmentation accuracy.
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Verma, Khushboo, Satwant Kumar et David Paydarfar. « Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR Images ». Diagnostics 12, no 9 (24 août 2022) : 2055. http://dx.doi.org/10.3390/diagnostics12092055.

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Lesion studies are crucial in establishing brain-behavior relationships, and accurately segmenting the lesion represents the first step in achieving this. Manual lesion segmentation is the gold standard for chronic strokes. However, it is labor-intensive, subject to bias, and limits sample size. Therefore, our objective is to develop an automatic segmentation algorithm for chronic stroke lesions on T1-weighted MR images. Methods: To train our model, we utilized an open-source dataset: ATLAS v2.0 (Anatomical Tracings of Lesions After Stroke). We partitioned the dataset of 655 T1 images with manual segmentation labels into five subsets and performed a 5-fold cross-validation to avoid overfitting of the model. We used a deep neural network (DNN) architecture for model training. Results: To evaluate the model performance, we used three metrics that pertain to diverse aspects of volumetric segmentation, including shape, location, and size. The Dice similarity coefficient (DSC) compares the spatial overlap between manual and machine segmentation. The average DSC was 0.65 (0.61–0.67; 95% bootstrapped CI). Average symmetric surface distance (ASSD) measures contour distances between the two segmentations. ASSD between manual and automatic segmentation was 12 mm. Finally, we compared the total lesion volumes and the Pearson correlation coefficient (ρ) between the manual and automatically segmented lesion volumes, which was 0.97 (p-value < 0.001). Conclusions: We present the first automated segmentation model trained on a large multicentric dataset. This model will enable automated on-demand processing of MRI scans and quantitative chronic stroke lesion assessment.
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Rossi, Farli. « APPLICATION OF A SEMI-AUTOMATED TECHNIQUE IN LUNG LESION SEGMENTATION ». Jurnal Teknoinfo 15, no 1 (15 janvier 2021) : 56. http://dx.doi.org/10.33365/jti.v15i1.945.

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Segmentation is one of the most important steps in automated medical diagnosis applications, which affects the accuracy of the overall system. In this study, we apply a semi-automated technique that combines an active contour and low-level processing techniques in lung lesion segmentation by extracting lung lesions from thoracic Positron Emission Tomography (PET)/Computed Tomography (CT) images. The lesions were first segmented in Positron Emission Tomography (PET) images which have been converted previously to Standardised Uptake Values (SUVs). The segmented PET images then serve as an initial contour for subsequent active contour segmentation of corresponding CT images. To measure accuracy, the Jaccard Index (JI) was used. Jaccard Index (JI) was calculated by comparing the segmented lesion to alternative segmentations obtained from the QIN lung CT segmentation challenge, which is possible by registering the whole body PET/CT images to the corresponding thoracic CT images. The results showed that the semi-automated technique (combination techniques between an active contour and low-level processing) in lung lesion segmentation has moderate accuracy with an average JI value of 0.76±0.12.
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Abdullah, Bassem A., Akmal A. Younis et Nigel M. John. « Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs ». Open Biomedical Engineering Journal 6, no 1 (9 mai 2012) : 56–72. http://dx.doi.org/10.2174/1874120701206010056.

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In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.
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Wang, Xueling, Xianmin Meng et Shu Yan. « Deep Learning-Based Image Segmentation of Cone-Beam Computed Tomography Images for Oral Lesion Detection ». Journal of Healthcare Engineering 2021 (21 septembre 2021) : 1–7. http://dx.doi.org/10.1155/2021/4603475.

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This paper aimed to study the adoption of deep learning (DL) algorithm of oral lesions for segmentation of cone-beam computed tomography (CBCT) images. 90 patients with oral lesions were taken as research subjects, and they were grouped into blank, control, and experimental groups, whose images were treated by the manual segmentation method, threshold segmentation algorithm, and full convolutional neural network (FCNN) DL algorithm, respectively. Then, effects of different methods on oral lesion CBCT image recognition and segmentation were analyzed. The results showed that there was no substantial difference in the number of patients with different types of oral lesions among three groups ( P > 0.05 ). The accuracy of lesion segmentation in the experimental group was as high as 98.3%, while those of the blank group and control group were 78.4% and 62.1%, respectively. The accuracy of segmentation of CBCT images in the blank group and control group was considerably inferior to the experimental group ( P < 0.05 ). The segmentation effect on the lesion and the lesion model in the experimental group and control group was evidently superior to the blank group ( P < 0.05 ). In short, the image segmentation accuracy of the FCNN DL method was better than the traditional manual segmentation and threshold segmentation algorithms. Applying the DL segmentation algorithm to CBCT images of oral lesions can accurately identify and segment the lesions.
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Xiong, Hui, Laith R. Sultan, Theodore W. Cary, Susan M. Schultz, Ghizlane Bouzghar et Chandra M. Sehgal. « The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images ». Ultrasound 25, no 2 (25 janvier 2017) : 98–106. http://dx.doi.org/10.1177/1742271x17690425.

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Purpose To assess the diagnostic performance of a leak-plugging segmentation method that we have developed for delineating breast masses on ultrasound images. Materials and methods Fifty-two biopsy-proven breast lesion images were analyzed by three observers using the leak-plugging and manual segmentation methods. From each segmentation method, grayscale and morphological features were extracted and classified as malignant or benign by logistic regression analysis. The performance of leak-plugging and manual segmentations was compared by: size of the lesion, overlap area ( Oa) between the margins, and area under the ROC curves ( Az). Results The lesion size from leak-plugging segmentation correlated closely with that from manual tracing ( R2 of 0.91). Oa was higher for leak plugging, 0.92 ± 0.01 and 0.86 ± 0.06 for benign and malignant masses, respectively, compared to 0.80 ± 0.04 and 0.73 ± 0.02 for manual tracings. Overall Oa between leak-plugging and manual segmentations was 0.79 ± 0.14 for benign and 0.73 ± 0.14 for malignant lesions. Az for leak plugging was consistently higher (0.910 ± 0.003) compared to 0.888 ± 0.012 for manual tracings. The coefficient of variation of Az between three observers was 0.29% for leak plugging compared to 1.3% for manual tracings. Conclusion The diagnostic performance, size measurements, and observer variability for automated leak-plugging segmentations were either comparable to or better than those of manual tracings.
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Wang, Ying, Jie Su, Qiuyu Xu et Yixin Zhong. « A Collaborative Learning Model for Skin Lesion Segmentation and Classification ». Diagnostics 13, no 5 (28 février 2023) : 912. http://dx.doi.org/10.3390/diagnostics13050912.

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The automatic segmentation and classification of skin lesions are two essential tasks in computer-aided skin cancer diagnosis. Segmentation aims to detect the location and boundary of the skin lesion area, while classification is used to evaluate the type of skin lesion. The location and contour information of lesions provided by segmentation is essential for the classification of skin lesions, while the skin disease classification helps generate target localization maps to assist the segmentation task. Although the segmentation and classification are studied independently in most cases, we find meaningful information can be explored using the correlation of dermatological segmentation and classification tasks, especially when the sample data are insufficient. In this paper, we propose a collaborative learning deep convolutional neural networks (CL-DCNN) model based on the teacher–student learning method for dermatological segmentation and classification. To generate high-quality pseudo-labels, we provide a self-training method. The segmentation network is selectively retrained through classification network screening pseudo-labels. Specially, we obtain high-quality pseudo-labels for the segmentation network by providing a reliability measure method. We also employ class activation maps to improve the location ability of the segmentation network. Furthermore, we provide the lesion contour information by using the lesion segmentation masks to improve the recognition ability of the classification network. Experiments are carried on the ISIC 2017 and ISIC Archive datasets. The CL-DCNN model achieved a Jaccard of 79.1% on the skin lesion segmentation task and an average AUC of 93.7% on the skin disease classification task, which is superior to the advanced skin lesion segmentation methods and classification methods.
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Liang, Yingbo, et Jian Fu. « Watershed Algorithm for Medical Image Segmentation Based on Morphology and Total Variation Model ». International Journal of Pattern Recognition and Artificial Intelligence 33, no 05 (8 avril 2019) : 1954019. http://dx.doi.org/10.1142/s0218001419540193.

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The traditional watershed algorithm has the limitation of false mark in medical image segmentation, which causes over-segmentation and images to be contaminated by noise possibly during acquisition. In this study, we proposed an improved watershed segmentation algorithm based on morphological processing and total variation model (TV) for medical image segmentation. First of all, morphological gradient preprocessing is performed on MRI images of brain lesions. Secondly, the gradient images are denoised by the all-variational model. While retaining the edge information of MRI images of brain lesions, the image noise is reduced. And then, the internal and external markers are obtained by forced minimum technique, and the gradient amplitude images are corrected by using these markers. Finally, the modified gradient image is subjected to watershed transformation. The experiment of segmentation and simulation of brain lesion MRI image is carried out on MATLAB. And the segmentation results are compared with other watershed algrothims. The experimental results demonstrate that our method obtains the least number of regions, which can extract MRI images of brain lesions effectively. In addition, this method can inhibit over-segmentation, improving the segmentation results of lesions in MRI images of brain lesions.
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Kaur, Manpreet, Sunitha Varghese, Leon Jekel, Niklas Tillmanns, Sara Merkaj, Khaled Bousabarah, MingDe Lin, Jitendra Bhawnani, Veronica Chiang et Mariam Aboian. « NIMG-07. APPLYING A GLIOMA-TRAINED DEEP LEARNING AUTO-SEGMENTATION TOOL ON BM PRE- AND POST-RADIOSURGERY ». Neuro-Oncology 24, Supplement_7 (1 novembre 2022) : vii162—vii163. http://dx.doi.org/10.1093/neuonc/noac209.626.

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Abstract PURPOSE Stereotactic radiosurgery (SRS) has become the mainstay to treat BM. Follow-up MRI provides important information on lesion treatment response and guides future therapy planning. Volumetric measurements of BM have shown promise over traditional uni- and two-dimensional measurements in more accurate and repeatable assessment. However, routine clinical use has yet to be achieved because the workflow is laborious. In previous work, we developed a PACS-integrated deep learning algorithm for automatic high- and low-grade glioma 3D segmentation. In this work, we applied this U-Net to segment BM on pre- and post-Gamma Knife (GK) MRI and evaluated the performance. METHODS 10 pre- and post-GK studies were autosegmented in five randomly selected patients (melanoma n= 3, breast n= 2). The glioma trained algorithm segmented the “Whole Tumor” (tumor core+peritumoral edema on T2w-FLAIR) and “Tumor Core” (CE tumor core+necrosis on SPGR). The AI generated segmentation was then revised as needed by a board-certified neuroradiologist and the dice-similarity-coefficient (DSC) between the revised and automatic volumetric segmentations were calculated. RESULTS Four patients had multicentric (2-4 BM) lesions. The mean± SD DSC for Whole Tumor and Tumor Core were 0.92±0.06 and 0.46±0.30 for pretreatment, 0.84±0.09 and 0.41±0.25 for posttreatment BM, respectively. The tool detected lesions with a sensitivity of 45% (5/11) for pretreatment and 50% (3/6) for posttreatment lesions. Three pretreatment and all posttreatment lesions that were not detected by the autosegmentation tool showed a very faint hyperintense peritumoral edema in T2w-FLAIR. CONCLUSION Volumetric segmentation of edema on FLAIR using the glioma-trained segmentation algorithm on pre- and post-GK BM did not require major adjustment of segmentation if it detects the lesion. On the other hand, with low sensitivity of lesion detection and low DSC for enhancing component, dedicated training of the algorithm on annotated BM data will be needed.
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Mechrez, Roey, Jacob Goldberger et Hayit Greenspan. « Patch-Based Segmentation with Spatial Consistency : Application to MS Lesions in Brain MRI ». International Journal of Biomedical Imaging 2016 (2016) : 1–13. http://dx.doi.org/10.1155/2016/7952541.

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This paper presents an automatic lesion segmentation method based on similarities between multichannel patches. A patch database is built using training images for which the label maps are known. For each patch in the testing image,ksimilar patches are retrieved from the database. The matching labels for thesekpatches are then combined to produce an initial segmentation map for the test case. Finally an iterative patch-based label refinement process based on the initial segmentation map is performed to ensure the spatial consistency of the detected lesions. The method was evaluated in experiments on multiple sclerosis (MS) lesion segmentation in magnetic resonance images (MRI) of the brain. An evaluation was done for each image in the MICCAI 2008 MS lesion segmentation challenge. Results are shown to compete with the state of the art in the challenge. We conclude that the proposed algorithm for segmentation of lesions provides a promising new approach for local segmentation and global detection in medical images.
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Thèses sur le sujet "Lesions segmentation"

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Abdullah, Bassem A. « Segmentation of Multiple Sclerosis Lesions in Brain MRI ». Scholarly Repository, 2012. http://scholarlyrepository.miami.edu/oa_dissertations/711.

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Multiple Sclerosis (MS) is an autoimmune disease of central nervous system. It may result in a variety of symptoms from blurred vision to severe muscle weakness and degradation, depending on the affected regions in brain. To better understand this disease and to quantify its evolution, magnetic resonance imaging (MRI) is increasingly used nowadays. Manual delineation of MS lesions in MR images by human expert is time-consuming, subjective, and prone to inter-expert variability. Therefore, automatic segmentation is needed as an alternative to manual segmentation. However, the progression of the MS lesions shows considerable variability and MS lesions present temporal changes in shape, location, and area between patients and even for the same patient, which renders the automatic segmentation of MS lesions a challenging problem. In this dissertation, a set of segmentation pipelines are proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. These techniques use a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The main contribution of this set of frameworks is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional views segmentation to produce verified segmentation. The multi-sectional views pipeline is customized to provide better segmentation performance and to benefit from the properties and the nature of MS lesion in MRI. These customization and enhancement leads to development of the customized MV-T-SVM. The MRI datasets that were used in the evaluation of the proposed pipelines are simulated MRI datasets (3 subjects) generated using the McGill University BrainWeb MRI Simulator, real datasets (51 subjects) publicly available at the workshop of MS Lesion Segmentation Challenge 2008 and real MRI datasets (10 subjects) for MS subjects acquired at the University of Miami. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.
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Naeslund, Elin. « Stroke Lesion Segmentation for tDCS ». Thesis, Linköpings universitet, Medicinsk informatik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71472.

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Transcranial direct current stimulation (tDCS), together with speech therapy, is known to relieve the symptoms of aphasia. Knowledge about amount of current to apply and stimulation location is needed to ensure the best result possible. Segmented tissues are used in a finite element method (FEM) simulation and by creating a mesh, information to guide the stimulation is gained. Thus, correct segmentation is crucial. Manual segmentation is known to produce the most accurate result, although it is not useful in the clinical setting since it currently takes weeks to manually segment one image volume. Automatic segmentation is faster, although both acute stroke lesions and nectrotic stroke lesions are known to cause problems. Three automatic segmentation routines are evaluated using default settings and two sets of tissue probability maps (TPMs). Two sets of stroke patients are used; one set with acute stroke lesions (which can only be seen as a change in image intensity) and one set with necrotic stroke lesions (which are cleared out and filled with cerebrospinal fluid (CSF)). The original segmentation routine in SPM8 does not produce correct segmentation result having problems with lesion and paralesional areas. Mohamed Seghier’s ALI, an automatic segmentation routine developed to handle lesions as an own tissue class, does not produce satisfactory result. The new segmentation routine in SPM8 produces the best results, especially if Chris Rorden’s (professor at The Georgia Institute of Technology) improved TPMs are used. Unfortunately, the layer of CSF is not continuous. The segmentation result can still be used in a FEM simulation, although the result from the simulatation will not be ideal. Neither of the automatic segmentation routines evaluated produce an acceptable result (see Figure 5.7) for stroke patients. Necrotic stroke lesions does not affect the segmentation result as much as the acute dito, especially if there is only a small amount of scar tissue present at the lesion site. The new segmentation routine in SPM8 has the brightest future, although changes need to be made to ensure anatomically correct segmentation results. Post-processing algorithms, relying on morphological prior constraints, can improve the segmentation result further.
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Wan, Fengkai. « Deep Learning Method used in Skin Lesions Segmentation and Classification ». Thesis, KTH, Medicinsk teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233467.

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Malignant melanoma (MM) is a type of skin cancer that is associated with a very poor prognosis and can often lead to death. Early detection is crucial in order to administer the right treatment successfully but currently requires the expertise of a dermatologist. In the past years, studies have shown that automatic detection of MM is possible through computer vision and machine learning methods. Skin lesion segmentation and classification are the key methods in supporting automatic detection of different skin lesions. Compared with traditional computer vision as well as other machine learning methods, deep neural networks currently show the greatest promise both in segmentation and classification. In our work, we have implemented several deep neural networks to achieve the goals of skin lesion segmentation and classification. We have also applied different training schemes. Our best segmentation model achieves pixel-wise accuracy of \textbf{0.940}, Dice index of \textbf{0.867} and Jaccard index of \textbf{0.765} on the ISIC 2017 challenge dataset. This surpassed the official state of the art model whose pixel-wise accuracy was 0.934, Dice index 0.849 and Jaccard Index 0.765. We have also trained a segmentation model with the help of adversarial loss which improved the baseline model slightly. Our experiments with several neural network models for skin lesion classification achieved varying results. We also combined both segmentation and classification in one pipeline meaning that we were able to train the most promising classification model on pre-segmented images. This resulted in improved classification performance. The binary (melanoma or not) classification from this single model trained without extra data and clinical information reaches an area under the curve (AUC) of 0.684 on the official ISIC test dataset. Our results suggest that automatic detection of skin cancers through image analysis shows significant promise in early detection of malignant melanoma.
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Cabezas, Grebol Mariano. « Atlas-based segmentation of multiple sclerosis lesions in magnetic resonance imaging ». Doctoral thesis, Universitat de Girona, 2013. http://hdl.handle.net/10803/119608.

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This thesis deals with the segmentation of brain magnetic resonance imaging applied to multiple sclerosis patients. This disease is characterised by the presence of white matter lesions in this image modality. After a thorough analysis of the state-of-the-art on this topic, pointing out the importance of prior knowledge, and a subsequent review of atlas-based segmentation of brain imaging, we propose two different multiple sclerosis lesion segmentation pipelines based on the conclusions of these studies. The first one provides an initial tissue classification using a modified expectation-maximisation algorithm, which is later on refined with a lesion segmentation step based on thresholding and a regionwise false positive reduction approach. The second one focuses only on the segmentation of lesions and uses an ensemble classifier alongside a rich feature pool including image intensities, probabilistic atlas maps, an outlier map and contextual information. Both approaches are tested against a novel database comprising imaging data from three different hospitals with a variable lesion load per case. The evaluation, carried out in a quantitative and qualitative manner, includes a comparison and uses several metrics for detection and segmentation. The analysis of the results points out a better performance relative to state-of-the-art approaches, with a clear improvement on the first pipeline in terms of detection, and a clear improvement on the second pipeline in terms of segmentation
Aquesta tesi es centra en la segmentació de imatges de ressonància magnètica del cervell aplicada a pacients d'esclerosi múltiple. Aquesta malaltia es caracteritza per l'aparició de lesions de matèria blanca, visibles en aquesta modalitat d'imatge. Després d'un anàlisi exhaustiu de l'estat de l'art en aquest tòpic, remarcant la importància de la informació prèvia, i també de la segmentació basada en atles del cervell, proposem dues estratègies diferents per a la segmentació de lesions basades en les conclusions d'ambdós estudis. La primera proporciona una classificació inicial dels teixits mitjançant una extensió de l'algorisme d'esperança-maximització, que es refina posteriorment amb un procés de segmentació de les lesions basat en una binarització inicial i una conseqüent estratègia de reducció de falsos positius a nivell de regió. La segona proposta es focalitza bàsicament en la segmentació de lesions i utilitza una combinació de classificadors febles entrenats amb un ric conjunt de característiques que inclou imatges d'intensitat, mapes probabilístics provinents d'un atles, un mapa d'intensitats atípiques i informació contextual. Ambdues estratègies han estat provades amb una nova base de dades formada per imatges de tres hospitals diferents amb diferent càrrega lesional per cas. L'avaluació d'aquestes proves, que s'ha dut a terme de forma quantitativa i qualitativa, inclou una comparativa i utilitza diferents mètriques de detecció i segmentació. L'anàlisi d'aquests resultats apunta a un millor rendiment relatiu a l'estat de l'art actual, amb una millor detecció per part de la primera estratègia i una millor segmentació per part de la segona
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Ma, Pu. « Automatic segmentation of multiple sclerosis lesions in magnetic resonance brain images ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ63536.pdf.

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García-Lorenzo, Daniel. « Robust Segmentation of Focal Lesions on Multi-Sequence MRI in Multiple Sclerosis ». Phd thesis, Université Rennes 1, 2010. http://tel.archives-ouvertes.fr/tel-00485645.

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La sclérose en plaques (SEP) atteint autour de 80.000 personnes en France. L'imagerie par résonance magnétique (IRM) est un outil essentiel pour le diagnostic de la SEP. Plusieurs bio-marqueurs sont obtenus à partir des IRM, comme le volume des lésions, et sont utilisés comme mesure dans des études cliniques en SEP, notamment pour le développement des nouveaux traitements. La segmentation manuelle des lésions est une tâche encombrante et dont les variabilités intra- et inter-expert sont grandes. Nous avons développé une chaîne de traitement automatique pour la segmentation des lesions focales en SEP. La méthode de segmentation est basée sur l'estimation robuste d'un modèle paramétrique des intensités du cerveau qui permet de détecter les lésions comme des données aberrantes. Nous avons aussi proposé deux méthodes pour ajouter de l'information spatiale avec les algorithmes mean shift et graph cut. Nous avons validé quantitativement notre approche en utilisant des images synthétiques et cliniques, provenant de deux centres différents pour évaluer la précision et la robustesse.
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García, Lorenzo Daniel. « Robust segmentation of focal lesions on multi-sequence MRI in multiple sclerosis ». Rennes 1, 2010. http://www.theses.fr/2010REN1S018.

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La sclérose en plaques (SEP) atteint autour de 80. 000 personnes en France. L'imagerie par résonance magnétique (IRM) est un outil essentiel pour le diagnostic de la SEP. Plusieurs bio-marqueurs sont obtenus à partir des IRM, comme le volume des lésions, et sont utilisés comme mesure dans des études cliniques en SEP, notamment pour le développement des nouveaux traitements. La segmentation manuelle des lésions est une tâche encombrante et dont les variabilités intra- et inter-expert sont grandes. Nous avons développé une chaîne de traitement automatique pour la segmentation des lesions focales en SEP. La méthode de segmentation est basée sur l'estimation robuste d'un modèle paramétrique des intensités du cerveau qui permet de détecter les lésions comme des données aberrantes. Nous avons aussi proposé deux méthodes pour ajouter de l'information spatiale avec les algorithmes mean shift et graph cut. Nous avons validé quantitativement notre approche en utilisant des images synthétiques et cliniques, provenant de deux centres différents pour évaluer la précision et la robustesse
Multiple sclerosis (MS) affects around 80. 000 people in France. Magnetic resonance imaging (MRI) is an essential tool for diagnosis of MS and MRI-derived surrogate markers such as MS lesion volumes are often used as measures in MS clinical trials for the development of new treatments. The manual segmentation of these MS lesions is a time-consuming task that shows high inter- and intra-rater variability. We developed an automatic workflow for the segmentation of focal MS lesions on MRI. The segmentation method is based on the robust estimation of a parametric model of the intensities of the brain; lesions are detected as outliers to the model. We proposed two methods to include spatial information in the segmentation using mean shift and graph cut. We performed a quantitative evaluation of our workflow using synthetic and clinical images of two different centers to verify its accuracy and robustness
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ASLANI, SHAHAB. « Deep learning approaches for segmentation of multiple sclerosis lesions on brain MRI ». Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/997626.

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Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system which causes lesions in brain tissues, especially visible in white matter with magnetic resonance imaging (MRI). The diagnosis of MS lesions, which is often performed visually with MRI, is an important task as it can help characterizing the progression of the disease and monitoring the efficacy of a candidate treatment. automatic detection and segmentation of MS lesions from MRI images offer the potential for a faster and more cost-effective performance which could also be immune to expert bias segmentation. In this thesis, we study automated approaches to segment MS lesions from MRI images. The thesis begins with a review of the existing literature on MS lesion segmentation and discusses their general limitations. We then propose three novel approaches that rely on Convolutional Neural Networks (CNNs) to segment MS lesions. The first approach demonstrates that the parameters of a CNN learned from natural images, transfer well to the tasks of MS lesion segmentation. In the second approach, we describe a novel multi-branch CNN architecture with end-to-end training that can take advantage of each MRI modalities individually. In that work, we also investigated the combination of MRI modalities leading to the best segmentation performance. In the third approach, we show an effective and novel generalization method for MS lesion segmentation when data are collected from multiple MRI scanning sites and as suffer from (site-)domain shifts. Finally, this thesis concludes with open questions that may benefit from future work. This thesis demonstrates the potential role of CNNs as a common methodological building block to address clinical problems in MS segmentation.
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Peruch, Francesco. « (SEMI)-AUTOMATED ANALYSIS OF MELANOCYTIC LESIONS ». Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424252.

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Melanoma is a very aggressive form of skin cancer whose incidence has constantly grown in the last 50 years. To increase the survival rate, an early diagnosis followed by a prompt excision is crucial and requires an accurate and periodic analysis of the patient's melanocytic lesions. We have developed an hardware and software solution named Mole Mapper to assist the dermatologists during the diagnostic process. The goal is to increase the accuracy of the diagnosis, accelerating the entire process at the same time. This is achieved through an automated analysis of the dermatoscopic images which computes and highlights the proper information to the dermatologist. In this thesis we present the 3 main algorithms that have been implemented into the Mole Mapper: A robust segmentation of the melanocytic lesion, which is the starting point for any other image processing algorithm and which allows the extraction of useful information about the lesion's shape and size. It outperforms the speed and quality of other state-of-the-art methods, with a precision that meets a Senior Dermatologist's standard and an execution time that allows for real-time video processing; A virtual shaving algorithm, which increases the precision and robustness of the other computer vision algorithms and provides the dermatologist with a hair-free image to be used during the evaluation process. It matches the quality of state-of-the-art methods but requires only a fraction of the computational time, allowing for computation on a mobile device in a time-frame compatible with an interactive GUI; A registration algorithm through which to study the evolution of the lesion over time, highlighting any unexpected anomalies and variations. Since a standard approach to this problem has not yet been proposed, we define the scope and constraints of the problem; we analyze the results and issues of standard registration techniques; and finally, we propose an algorithm with a speed compatible with Mole Mapper's constraints and with an accuracy comparable to the registration performed by a human operator.
Il Melanoma è una forma molto aggressiva di cancro alla pelle la cui incidenza è costantemente aumentata negli ultimi 50 anni. Una diagnosi precoce unita ad una rapida asportazione risulta indispensabile per migliorare il tasso di sopravvivenza e richiede una analisi periodica ed accurata della lesioni melanocitiche del paziente. Abbiamo sviluppato una soluzione hardware e software chiamata Mole Mapper per assistere i deramtologi durante l'intero processo di diagnosi. L'obiettivo è permettere un incremento dell'accuratezza della diagnosi velocizzando al contempo l'intero processo. Tali caratteristiche si sono ottenute grazie ad un'analisi automatica delle immagini dermatoscopiche che individua ed evidenza al dermatologo le informazioni più significative. In questa tesi presentiamo 3 principali algoritmi che sono stati implementati in Mole Mapper: Una robusta segmentazione di lesioni melanocitiche, che risulta il punto di partenza di ogni altro algoritmo di elaborazioni di immagini e permette l'estrazione di informazioni utili riguardanti la forma e la dimensione delle lesioni. Tale algoritmo supera in accuratezza e velocità lo stato dell'arte attuale, con una precisione paragonabile ad un dermatologo esperto ed un tempo di esecuzione compatibile con l'elaborazione video realtime; Un algoritmo di depilazione digitale, che garantisce miglior precisione e robustezza agli altri algoritmi di elaborazione di immagini a fornisce al dermatologo un immagine priva di peli da impiegare nel processo di valutazione. La nostra proposta supera l'accuratezza dello stato dell'arte richiedendo solo una frazione del tempo di esecuzione, tanto da poter essere integrata su dispositivi mobili all'interno di una GUI interattiva. Un algoritmo di registrazione, per studiare l'evoluzione delle lesioni nel tempo evidenziando ogni possibile anomalia o variazione. Data la mancanza di un approccio standard al problema, abbiamo caratteriizzato gli obbiettivi ed i vincoli a cui sottostare proponendo quindi un approccio con un tempo di esecuzione compatibile con le necessità del Mole Mapper ed un accuratezza paragonabile a quella di un operatore umano.
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Cui, Shenshen. « Fully Automatic Segmentation of White Matter Lesions from Multispectral Magnetic Resonance Imaging Data ». Thesis, Uppsala University, Department of Information Technology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-122650.

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A fully automatic white matter lesion segmentation method has been developed and evaluated. The method uses multispectral magnetic resonance imaging (MRI) data (T1,T2 and Proton Density). First fuzzy c means (FCM) was used to segment normal brain tissues (white matter,grey matter, and cerebrospinal fluid). The holes in normal white matter were used to sample the WML intensities in the different images. The segmentation of WML was optimized by a graph cut approach. The method was trained by using 9 manually segmented datasets and evaluated by comparison to 11 other manually segmented, and visually evaluated, datasets. The graph cut part of the automatic segmentation requires, on average, 30 seconds per dataset. The results correlated well (r=0.954) to a manually created reference that was supervised by two neuro radiologists.

Key Words: White matter lesion, automatic segmentation, graph cuts, MRI, PIVUS

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Chapitres de livres sur le sujet "Lesions segmentation"

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Kruggel, Frithjof. « Segmentation of Focal Brain Lesions ». Dans Lecture Notes in Computer Science, 10–18. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28626-4_2.

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Rodríguez, Roberto, et Oriana Pacheco. « A Strategy for Atherosclerotic Lesions Segmentation ». Dans Lecture Notes in Computer Science, 171–80. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11578079_19.

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Sharma, Rakesh, Jasjit S. Suri et Ponnada A. Narayana. « Segmentation Techniques in the Quantification of Multiple Sclerosis Lesions in MRI ». Dans Advanced Algorithmic Approaches to Medical Image Segmentation, 318–40. London : Springer London, 2002. http://dx.doi.org/10.1007/978-0-85729-333-6_5.

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Ma, Zhen, et João Manuel R. S. Tavares. « Segmentation of Skin Lesions Using Level Set Method ». Dans Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications, 228–33. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09994-1_20.

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Zeng, Ziming, et Reyer Zwiggelaar. « Joint Histogram Modelling for Segmentation Multiple Sclerosis Lesions ». Dans Computer Vision/Computer Graphics Collaboration Techniques, 133–44. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24136-9_12.

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Shahar, Allon, et Hayit Greenspan. « Probabilistic Spatial-Temporal Segmentation of Multiple Sclerosis Lesions ». Dans Lecture Notes in Computer Science, 269–80. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27816-0_23.

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Nageswaran, Sharmila, S. Vidhya et Deepa Madathil. « Segmentation and Clinical Outcome Prediction in Brain Lesions ». Dans Machine Learning and IoT, 181–91. Boca Raton : Taylor & Francis, 2019. : CRC Press, 2018. http://dx.doi.org/10.1201/9781351029940-11.

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Franco, M. L. N., L. M. Nunes, A. P. P. Froner, A. M. M. Silva et A. C. Patrocinio. « Influence of ROI pattern on segmentation in lung lesions ». Dans IFMBE Proceedings, 211–14. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19387-8_51.

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Kubicek, Jan, Iveta Bryjova, Marek Penhaker, Jana Javurkova et Lukas Kolarcik. « Segmentation of Macular Lesions Using Active Shape Contour Method ». Dans Advances in Intelligent Systems and Computing, 213–21. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27644-1_20.

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Adegun, Adekanmi, et Serestina Viriri. « An Enhanced Deep Learning Framework for Skin Lesions Segmentation ». Dans Computational Collective Intelligence, 414–25. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28377-3_34.

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Actes de conférences sur le sujet "Lesions segmentation"

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Ibarrola Chamorro, Claudia Raquel, et Wagner Coelho de Albuquerque Pereira. « Segmentation of Mammary Lesions in Ultrasound Images Applying Mask R-CNN ». Dans Congresso Latino-Americano de Software Livre e Tecnologias Abertas. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/latinoware.2019.10352.

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Breast cancer is the most frequent malignant tumor in women and one of the most common in the world. One of the most important issues in this condition is early detection. Computer-aided diagnostic (CAD) systems are objects of research, aiming to provide a second opinion to the health professional. A fundamental aspect within the CAD system is the segmentation of the lesion, allowing an adequate extraction of the lesion characteristics. The use of a computerized segmentation method helps eliminate human variability and, consequently, improve the performance of the lesion classifier. Convolutional Neural Networks (CNNs) are being used in segmentation problems, such as various types of medical imaging, people and road signs detection, for example. So inspired by these promissing results, the present work has as main objective to analyze and implement the Mask R-CNN as a tool of segmentation of mammary lesions in images obtained by ultrasound, to propose an efficient method of segmentation, aiding in the classification process of the CAD systems.
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Wen, AiQing, Xiaochuan Chen, Anni Chen, Hongyu Shi et Yuming Hong. « Segmentation of kidney lesions with attention model based on Deeplab ». Dans 2019 Kidney Tumor Segmentation Challenge : KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.032.

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Ramli, Roshaslinie, Aamir Saeed Malik, Ahmad Fadzil M. Hani et Felix Boon-Bin Yap. « Segmentation of Acne Vulgaris Lesions ». Dans 2011 International Conference on Digital Image Computing : Techniques and Applications (DICTA). IEEE, 2011. http://dx.doi.org/10.1109/dicta.2011.63.

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Ning, Yang, Chenbo Shi, Li Wang et Chang Shu. « Automatic segmentation of psoriasis lesions ». Dans SPIE/COS Photonics Asia, sous la direction de Qionghai Dai et Tsutomu Shimura. SPIE, 2014. http://dx.doi.org/10.1117/12.2074372.

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Vesal, Sulaiman, Nishant Ravikumar et Andreas K. Maier. « KidNet : An Automated Framework for Renal Lesions Detection and Segmentation in CT Images ». Dans 2019 Kidney Tumor Segmentation Challenge : KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.051.

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Peris-Fajarnés, Guillermo, María Moncho Santonja, María Begoña Sanz Alamán, Beatriz Defez García et Ismael Lengua Lengua. « Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images ». Dans INNODOCT 2019. Valencia : Universitat Politècnica de València, 2019. http://dx.doi.org/10.4995/inn2019.2019.10946.

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Acne vulgaris is one of the most common human pathologies worldwide. Its prevalence causes a high healthcare expenditure. Acne healthcare costs and effects on individuals' quality of life lead to the need of analysing current acne evaluation, treatment and monitoring methods. One of the most common ones is manual lesion counting by a dermatologist. However, this technique has several limitations, such as time spent. That is the reason why the development of new computer-assisted techniques are needed in order to automatically count the acne lesions. Nonetheless, the first step is automatic acne lesion detection on the skin of patients. The aim of this work is to propose a new methodology to solve the acne images segmentation problem, so that the implementation of a system for automatic counting is possible. The results would be a decrease in both time spent and diagnosis errors. With this objective, after doing a systematic review on the state of the art of acne images segmentation methods, fluorescence images of the face of acne patients are obtained. This image modality enhances visualization of the acne lesions. Finally, using the fluorescence images, a segmentation algorithm is implemented in MATLAB.
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Jazzar, Nesrine, et Ali Douik. « A New Deep-Net Architecture for Ischemic Stroke Lesion Segmentation ». Dans 4th International Conference on Machine Learning & Applications (CMLA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121108.

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Ischemic stroke, brain cells death due to a lack of oxygen, is a leading cause of long-term disability and death. Accurate diagnosis and timely intervention can effectively improve the blood supply of the ischemic stroke area and minimize brain damage. Recent studies have shown the potential to use magnetic resonance imaging (MRI) to provide contrast imaging to visualize and detect lesions. However, manual segmentation of the stroke lesion produced by MRI is a tedious and time-consuming task. Therefore, the automatic ischemic stroke lesion segmentation method may show excellent advantages. In this paper, we propose a novel deep learning method used to detect and localize brain ischemic stroke, a generalization encoderdecoder by modifying U-Net architecture. We integrate multi-path architecture into both encoder and decoder blocks to captures different levels of the encoded state, which helps in more robust decision-making for stroke lesion segmentation. In bottleneck of the architecture, we applied dilated blocks to improve the underlying predictive capabilities. The proposed method has been tested on the publicly accessible web platform provided by the MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge. The results demonstrate that the proposed method achieves a mean dice coefficient 0.91 of with the training and 0.84 with the testing data respectively.
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Masood, N. A., H. M. Mashali et Abdalla S. A. Mohamed. « Color Segmentation for Skin Lesions Classification ». Dans 2008 Cairo International Biomedical Engineering Conference (CIBEC). IEEE, 2008. http://dx.doi.org/10.1109/cibec.2008.4786059.

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Keceli, Ali Seydi, et Ahmet Burak Can. « Automatic segmentation of white matter lesions ». Dans 2009 IEEE 17th Signal Processing and Communications Applications Conference (SIU). IEEE, 2009. http://dx.doi.org/10.1109/siu.2009.5136362.

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Sáez, Aurora, Begoña Acha et Carmen Serrano. « Segmentation and classification of dermatological lesions ». Dans SPIE Medical Imaging, sous la direction de Nico Karssemeijer et Ronald M. Summers. SPIE, 2010. http://dx.doi.org/10.1117/12.844323.

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