Academic Journal
A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke
| العنوان: | A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke |
|---|---|
| المؤلفون: | Menze, Bjoern H, Van Leemput, Koen, Lashkari, Danial, Riklin-Raviv, Tammy, Geremia, Ezequiel, Alberts, Esther, Gruber, Philipp, Wegener, Susanne, Weber, Marc-André, Szekely, Gabor, Ayache, Nicholas, Golland, Polina |
| المساهمون: | Computer Science and Artificial Intelligence Laboratory Cambridge (CSAIL), Massachusetts Institute of Technology (MIT), Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Computer Vision Laboratory - ETHZ Zurich, Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology Zürich (ETH Zürich), Institute for Advanced Study Munich (TUM-IAS), Technical University of Munich (TUM), Department of radiology (Massachusetts General Hospital), Massachusetts General Hospital Boston, Universitätsspital Zürich (USZ), Diagnostic and Interventional Radiology Heidelberg, Heidelberg University Hospital Heidelberg, Division of Medical Physics in Radiology Heidelberg, German Cancer Research Center - Deutsches Krebsforschungszentrum Heidelberg (DKFZ) |
| المصدر: | ISSN: 0278-0062 ; IEEE Transactions on Medical Imaging ; https://hal.inria.fr/hal-01230846 ; IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2015. |
| بيانات النشر: | HAL CCSD Institute of Electrical and Electronics Engineers |
| سنة النشر: | 2015 |
| المجموعة: | Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
| مصطلحات موضوعية: | [SCCO.COMP]Cognitive science/Computer science |
| الوصف: | International audience ; We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| Relation: | hal-01230846; https://hal.inria.fr/hal-01230846; https://hal.inria.fr/hal-01230846/document; https://hal.inria.fr/hal-01230846/file/manuscript.pdf |
| الاتاحة: | https://hal.inria.fr/hal-01230846 https://hal.inria.fr/hal-01230846/document https://hal.inria.fr/hal-01230846/file/manuscript.pdf |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.F205AA56 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |