RECORD
X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies
Image and Video Processing · Submitted 16 Jul 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The morbidity of brain stroke increased rapidly in the past few years. | background 1.00 | 0.06 | 0.01 | asserted 1.00 |
| s1 | To help specialists in lesion measurements and treatment planning, automatic segmentation methods are critically required for clinical practices. | background 0.91 | 0.05 | 0.03 | asserted 1.00 |
| s2 | Recently, approaches based on deep learning and methods for contextual information extraction have served in many image segmentation tasks. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s3 | However, their performances are limited due to the insufficient training of a large number of parameters, which sometimes fail in capturing long-range dependencies. | gap 0.86 | 0.07 | 0.03 | asserted 0.87 |
| s4 | 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. | contribution 0.84 | 0.94 | 0.12 | asserted 0.99 |
| s5 | 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. | method 0.75 | 0.83 | 0.17 | asserted 0.97 |
| s6 | 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. | result 1.00 | 0.96 | 0.78 | asserted 1.00 |
| s7 | We make our code and models available at https://github.com/Andrewsher/X-Net. | contribution 0.79 | 0.74 | 0.02 | asserted 0.87 |
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The paper
Original abstract
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.
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We use the Adam [ref] method to optimize our model.
Adam: A Method for Stochastic Optimization · Implementation · 1.00
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