X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies (1907.07000v2)

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.

• The paper introduces X-Net, a novel model integrating depthwise separable convolutions and long-range dependency capture for efficient stroke lesion segmentation.
• It employs an encoder-decoder framework with a Feature Similarity Module to enhance contextual feature extraction and handle diverse lesion shapes.
• X-Net outperforms traditional models like U-Net in Dice score, IoU, and precision while significantly reducing trainable parameters for clinical efficiency.

Insights into X-Net: An Efficient Approach to Brain Stroke Lesion Segmentation

The paper "X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies" introduces a novel approach to automatic segmentation of brain stroke lesions in medical imaging, particularly through the deployment of depthwise separable convolution and long-range dependency capture. This research emphasizes the critical need for automatic segmentation methods in clinical practice, aimed at improving the efficiency of brain stroke lesion identification, which is fundamental given the rapid increase in the morbidity of strokes worldwide.

Methodology

The authors present the X-Net architecture, which involves an intricate integration of depthwise separable convolutions—a technique known for significantly reducing network parameters—into the established U-Net framework. The depthwise separable convolution reduces redundancy and computational expense while maintaining the efficacy of feature extraction. Concurrently, the Feature Similarity Module (FSM) is introduced to effectively capture long-range spatial dependencies by calculating relationships between pixels in the feature map, thereby enhancing the contextual understanding necessary for segmenting lesions with diverse shapes and sizes.

The X-Net employs an encoder-decoder strategy, boosting segmentation performance by interpolating encoder-derived complex features with decoder operations while actively utilizing skip connections. The FSM significantly aids in this process by condensing dense contextual information required for accurate lesion prediction, demonstrating the ability to address challenges like the variance in lesion characteristics and boundary ambiguity.

Evaluation and Results

The paper reports high-performance results achieved by the proposed X-Net against several state-of-the-art models using the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset. Notably, X-Net surpasses competitors such as U-Net, ResUNet, and 2D Dense-UNet in Dice score, Intersection over Union (IoU), and precision, while simultaneously boasting a substantial reduction in the number of trainable parameters—indicative of computational efficiency. This suggests a promising balance between accuracy and efficiency, vital for practical clinical applications where quick processing and reliable results are paramount.

Implications and Future Work

The implications of this research are profound, offering potential for substantial improvements in stroke lesion segmentation accuracy and speed in clinical settings, thus facilitating better-informed treatment planning. Moreover, the modular nature of FSM implies wide applicability across different fully convolutional networks, allowing for advancements in various medical image segmentation tasks.

Looking forward, development strategies could explore optimizing X-Net adaptations for other segmentation tasks, examining how such modular integration can be universally applied across modalities and pathologies. Furthermore, future research might explore enhancements in FSM design for even more refined context capture as well as expanding the architecture's scalability towards 3D segmentation tasks, which would broaden its utility in the medical imaging domain.

In summary, this paper makes robust contributions to the field of medical image segmentation, particularly in brain stroke lesions, highlighting the pivotal roles of depthwise separable convolutional frameworks and contextual information extraction through FSM. The results and theoretical implications of X-Net set a promising path towards more efficient, reliable, and practical automatic lesion segmentation methods in clinical environments.