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

arXiv: https://arxiv.org/abs/1907.07000 · HTML: https://mlsanity.com/paper/1907.07000/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2019.

## Built on by (most cited first, 20 of 20)

- [Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining](https://mlsanity.com/paper/2402.03302/) (2024)
- [DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2202.00972/) (2022)
- [Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2104.01896/) (2021)
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- [A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images](https://mlsanity.com/paper/2109.12634/) (2021)
- [SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization](https://mlsanity.com/paper/2205.04329/) (2022)
- [Small Lesion Segmentation in Brain MRIs with Subpixel Embedding](https://mlsanity.com/paper/2109.08791/) (2021)
- [Transformers-based architectures for stroke segmentation: A review](https://mlsanity.com/paper/2403.18637/) (2024)
- [DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation](https://mlsanity.com/paper/2105.07962/) (2021)
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- [ConvTransSeg: A Multi-resolution Convolution-Transformer Network for Medical Image Segmentation](https://mlsanity.com/paper/2210.07072/) (2022)
- [Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review](https://mlsanity.com/paper/2103.13588/) (2021)
- [R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation](https://mlsanity.com/paper/2105.02290/) (2021)
- [AGMR-Net: Attention Guided Multiscale Recovery framework for stroke segmentation](https://mlsanity.com/paper/2202.13687/) (2022)
- [FAN-Net: Fourier-Based Adaptive Normalization For Cross-Domain Stroke Lesion Segmentation](https://mlsanity.com/paper/2304.11557/) (2023)
- [BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision](https://mlsanity.com/paper/2310.07060/) (2023)
- [CV-Attention UNet: Attention-based UNet for 3D Cerebrovascular Segmentation of Enhanced TOF-MRA Images](https://mlsanity.com/paper/2311.10224/) (2023)
- [Stroke Lesion Segmentation with Visual Cortex Anatomy Alike Neural Nets](https://mlsanity.com/paper/2105.06544/) (2021)
- [RefLSM: Linearized Structural-Prior Reflectance Model for Medical Image Segmentation and Bias-Field Correction](https://mlsanity.com/paper/2512.07191/) (2025)
- [VRXU-net: A Deep Learning Approach for Brain Ischemic Stroke Lesion Detection and Segmentation in T1W MRI](https://mlsanity.com/paper/2605.21633/) (2026)

## Abstract (arXiv)

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.