A Multiple Encoders Network for Stroke Lesion Segmentation

Zhang, Xiangchen; Xu, Huan; Liu, Yujun; Liao, Jiajia; Cai, Guorong; Su, Jinhe; Song, Yehua

doi:10.1007/978-3-030-88010-1_44

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Liu et al [41] presented a Multi-scale Deep Fusion unit in the bottleneck layer of U-Net; the authors employed the techniques of Atrous Spatial Pyramid Pooling (ASPP) [74] and capsules with dynamic routing [75] to capture and encode the global context. Zhang et al [12] also employed the concept of ASPP in their bottleneck layer. The ASPP produced a fused feature map using the features produced from U-Net's encoding layer and the feature set produced by the residual encoder.…”

Section: Supervised Learningmentioning

confidence: 99%

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Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review

Malik,

Chong,

Fernandez

et al. 2024

Bioengineering

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Stroke is a medical condition that affects around 15 million people annually. Patients and their families can face severe financial and emotional challenges as it can cause motor, speech, cognitive, and emotional impairments. Stroke lesion segmentation identifies the stroke lesion visually while providing useful anatomical information. Though different computer-aided software are available for manual segmentation, state-of-the-art deep learning makes the job much easier. This review paper explores the different deep-learning-based lesion segmentation models and the impact of different pre-processing techniques on their performance. It aims to provide a comprehensive overview of the state-of-the-art models and aims to guide future research and contribute to the development of more robust and effective stroke lesion segmentation models.

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Section: Supervised Learningmentioning

confidence: 99%

“…However, some human intervention and verification is still required. It is also time-consuming and laborious to segment the lesions layer by layer [12], and the chances of error and bias are also high as lesions could be of irregular size and shape in each layer [7,13], which necessitates the development of automated techniques.…”

Section: Introductionmentioning

confidence: 99%

Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review

Malik,

Chong,

Fernandez

et al. 2024

Bioengineering

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“…The network is used to produce segmentation maps by deepening the network and fusing multi‐scale information. Zhang et al 23 proposed a lightweight multi‐encoder strategy to extract depth information through additional residual encoders and achieved excellent results in brain magnetic resonance imaging (MRI) images.…”

Section: Related Workmentioning

confidence: 99%

MSSA‐Net: A novel multi‐scale feature fusion and global self‐attention network for lesion segmentation

Huang

Zhang

Zhang³

et al. 2022

Concurrency and Computation

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In medical image segmentation tasks, it is typical to adopt convolutional neural networks with a serial encoder-decoder structure. However, mainstream networks cannot simultaneously achieve sufficient extraction of global features and the fusion of multi-scale information, which may lead to unpromising results for the segmentation of pathological images. Therefore, this article proposed a novel multi-scale feature fusion and global self-attention network (MSSA-Net) for medical image segmentation. Specifically, we designed a parallel double-encoder network with a multi-scale feature fusion encoder (MS-Encoder) and a self-attention encoder (SA-Encoder). The SA-Encoder introduces the transformer's global self-attention mechanism to extract global features, and the MS-Encoder adopts atrous spatial pyramid pooling (ASPP) to realize multi-scale fusion. We have evaluated the proposed MSSA-Net using three medical segmentation datasets, covering various imaging modalities such as colonoscopy and magnetic resonance imaging. Experiments on the CVC-ClinicDC, the 2015 MICCAI subchallenge on automatic polyp detection dataset, and anatomical tracings of lesions after stroke (ATLAS) show that our MSSA-Net outperforms mainstream methods such as DoubleU-Net and TransUNet. Moreover, MSSA-Net can predict more accurate segmentation masks, especially in the case of ATLAS, which has challenging images such as multiple shadow areas and discrete lesions.

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Segmentation of stroke lesions using transformers‐augmented MRI analysis

Ahmed,

Al Shehhi,

Werghi

et al. 2024

Human Brain Mapping

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Accurate segmentation of chronic stroke lesions from mono‐spectral magnetic resonance imaging scans (e.g., T1‐weighted images) is a difficult task due to the arbitrary shape, complex texture, variable size and intensities, and varied locations of the lesions. Due to this inherent spatial heterogeneity, existing machine learning methods have shown moderate performance for chronic lesion delineation. In this study, we introduced: (1) a method that integrates transformers' deformable feature attention mechanism with convolutional deep learning architecture to improve the accuracy and generalizability of stroke lesion segmentation, and (2) an ecological data augmentation technique based on inserting real lesions into intact brain regions. Our combination of these two approaches resulted in a significant increase in segmentation performance, with a Dice index of 0.82 (±0.39), outperforming the existing methods trained and tested on the same Anatomical Tracings of Lesions After Stroke (ATLAS) 2022 dataset. Our method performed relatively well even for cases with small stroke lesions. We validated the robustness of our method through an ablation study and by testing it on new unseen brain scans from the Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset. Overall, our proposed approach of transformers with ecological data augmentation offers a robust way to delineate chronic stroke lesions with clinically relevant accuracy. Our method can be extended to other challenging tasks that require automated detection and segmentation of diverse brain abnormalities from clinical scans.

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A Multiple Encoders Network for Stroke Lesion Segmentation

Cited by 4 publications

References 16 publications

Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review

Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review

MSSA‐Net: A novel multi‐scale feature fusion and global self‐attention network for lesion segmentation

Segmentation of stroke lesions using transformers‐augmented MRI analysis

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