X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies

The morbidity of brain stroke increased rapidly in the past few years. To help specialists in lesion measurements and treatment planning, automatic segmentation methods are critically required for clinical practices. Recently, approaches based on deep learning and methods for contextual information extraction have served in many image segmentation tasks. However, their performances are limited due to the insufficient training of a large number of parameters, which sometimes fail in capturing long-range dependencies. To address these issues, we propose a depthwise separable convolution based X-Net that designs a nonlocal operation namely Feature Similarity Module (FSM) to capture long-range dependencies. The adopted depthwise convolution allows to reduce the network size, while the developed FSM provides a more effective, dense contextual information extraction and thus facilitates better segmentation. The effectiveness of X-Net was evaluated on an open dataset Anatomical Tracings of Lesions After Stroke (ATLAS) with superior performance achieved compared to other six state-of-the-art approaches. We make our code and models available at https://github.com/Andrewsher/X-Net.Comment: MICCAI 201

Watershed parallel algorithm for asynchronous processors array

ISBN: 0780373049A joint algorithm-architecture analysis leads to a new version of picture segmentation system adapted to multimedia mobile terminal constraints. The asynchronous processors network, with a granularity level of one processor per pixel, based on data flow model, takes less than 10 mu s to segment a SQCIF $88*72 pixels - image (about 2000 times faster than the classical sequential watershed algorithms). The main originality of the proposed algorithm is only one global synchronization point is needed in order to complete the segmentation transformation, instead of the three (or more) classical points: minima detection, labelization and flooding. Our system tends to cope with multimedia mobile phones constraints, i.e. real time computing circuit, low power. We have simulated and validated this system thanks to "SystemC" library; VHDL synchronous prototyping shows up results accordingly

Réseau calculant asynchrone dédié à la segmentation d'images

National audienceUne étude conjointe algorithme-architecture débouche sur l'implantation de l'algorithme de segmentation par ascension de colline ("Hill Climbing") sous une forme parallèle à fine granularité (un processeur élémentaire par pixel) sur un réseau calculant : modèle où les communications et les calculs sont effectués simultanément. Seul le flot de données ordonne les phases de calcul locales. L'implantation micro´electronique du concept proposé permettrait de diviser le temps de calcul par 2 600 environ pour une image SQCIF (88*72 pixels)

Réseau calculant asynchrone dédié à la segmentation d'images

National audienceUne étude conjointe algorithme-architecture débouche sur l'implantation de l'algorithme de segmentation par ascension de colline ("Hill Climbing") sous une forme parallèle à fine granularité (un processeur élémentaire par pixel) sur un réseau calculant : modèle où les communications et les calculs sont effectués simultanément. Seul le flot de données ordonne les phases de calcul locales. L'implantation micro´electronique du concept proposé permettrait de diviser le temps de calcul par 2 600 environ pour une image SQCIF (88*72 pixels)