Prior knowledge-guided and border-focused segmentation of ischemic stroke lesions_Journal of Biomedical Engineering

Authors：

CUI Wencheng ,  WEN Zixin , SHAO Hong

School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China;

Corresponding?author：

WEN Zixin, Email: wenzixin0318@163.com

Keywords：

Stroke lesion segmentation; Prior knowledge; Edge feature extraction; Boundary constraints

DOI：

10.7507/1001-5515.202512008

Video：

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Magnetic resonance imaging plays a crucial role in the diagnosis and management of ischemic stroke. Accurate segmentation of stroke lesions holds significant clinical value in assisting the formulation of individualized interventional treatment plans and objectively assessing patient prognosis. To address the challenges of blurred and irregularly shaped ischemic stroke lesions with random locations, this study proposes a prior knowledge-guided and multi-level edge feature fusion shifted window Transformer-based U-Net with encoder representations (PMSwin UNETR). First, based on the distribution characteristics of stroke lesions, a lesion distribution probability map is generated to guide the segmentation network in focusing on areas prone to lesions. Second, a multi-level edge feature extraction module is employed to enrich edge features. Finally, a soft-clDice loss function is introduced to directly learn lesion boundaries, enhancing edge segmentation accuracy. The effectiveness of PMSwin UNETR was validated using the 2022 Ischemic Stroke Lesion Segmentation Challenge (ISLES2022) dataset, with results showing Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and recall rates of 82.43%, 3.768 8, and 82.45%, respectively, outperforming other mainstream segmentation algorithms. This study demonstrates that the proposed PMSwin UNETR model effectively improves ischemic stroke lesion segmentation, providing important references and application value for precise clinical diagnosis and intelligent medical imaging research in stroke.

Citation： CUI Wencheng, WEN Zixin, SHAO Hong. Prior knowledge-guided and border-focused segmentation of ischemic stroke lesions. Journal of Biomedical Engineering, 2026, 43(2): 362-372. doi: 10.7507/1001-5515.202512008 Copy

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2.	王隴德, 彭斌, 張鴻祺, 等. 《中國腦卒中防治報告2020》概要. 中國腦血管病雜志, 2022, 19(2): 136-144.
3.	劉樂, 余超, 廖逸文, 等. 1990-2019年中國缺血性腦卒中疾病負擔變化分析. 中國循證醫學雜志, 2022, 22(9): 993-998.
4.	中國卒中學會, 中國卒中學會神經介入分會, 中華預防醫學會卒中預防與控制專業委員會介入學組. 急性缺血性卒中血管內治療影像評估中國專家共識. 中國卒中雜志, 2017, 12(11): 1041-1056.
5.	霍曉川, 高峰. 急性缺血性卒中血管內治療中國指南2023. 中國卒中雜志, 2023, 18(6): 684-711.
6.	Miceli G, Rizzo G, Basso M G, et al. Artificial intelligence in symptomatic carotid plaque detection: a narrative review. Appl Sci, 2023, 13(7): 4321.
7.	王姍, 趙建華. 基于CT和MRI的影像組學在缺血性腦卒中的研究進展. CT理論與應用研究, 2024, 33(1): 83-89.
8.	Fiez J A, Damasio H, Grabowski T J. Lesion segmentation and manual warping to a reference brain: intra‐ and interobserver reliability. Hum Brain Mapp, 2000, 9(4): 192-211.
9.	Montaner J, Rovira A, Molina C A, et al. Plasmatic level of neuroinflammatory markers predict the extent of diffusion-weighted image lesions in hyperacute stroke. J Cereb Blood Flow Metab, 2003, 23(12): 1403-1407.
10.	Wittsack H J, Ritzl A, Fink G R, et al. MR imaging in acute stroke: diffusion-weighted and perfusion imaging parameters for predicting infarct size. Radiology, 2002, 222(2): 397-403.
11.	Pustina D, Coslett H B, Turkeltaub P E, et al. Automated segmentation of chronic stroke lesions using LINDA: lesion identification with neighborhood data analysis. Hum Brain Mapp, 2016, 37(4): 1405-1421.
12.	Karthik R, Menaka R, Johnson A, et al. Neuroimaging and deep learning for brain stroke detection: a review of recent advancements and future prospects. Comput Methods Programs Biomed, 2020, 197: 105728.
13.	Luo J, Dai P, He Z, et al. Deep learning models for ischemic stroke lesion segmentation in medical images: a survey. Comput Biol Med, 2024, 175: 108509.
14.	Zhou Y, Huang W, Dong P, et al. D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation. IEEE/ACM Trans Comput Biol Bioinform, 2021, 18(3): 940-950.
15.	Liu L, Kurgan L, Wu F X, et al. Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease. Med Image Anal, 2020, 65: 101791.
16.	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
17.	Huang B, Tan G, Dou H, et al. Mutual gain adaptive network for segmenting brain stroke lesions. Appl Soft Comput, 2022, 129: 109568.
18.	Li L, Ma K, Song Y, et al. TSRL-Net: target-aware supervision residual learning for stroke segmentation. Comput Biol Med, 2023, 159: 106840.
19.	Nie X, Liu X, Yang H, et al. Fully automatic identification of post-treatment infarct lesions after endovascular therapy based on non-contrast computed tomography. Neural Comput Appl, 2023, 35(30): 22101-22114.
20.	Ashtari P, Sima D M, De Lathauwer L, et al. Factorizer: a scalable interpretable approach to context modeling for medical image segmentation. Med Image Anal, 2023, 84: 102706.
21.	Liu L, Chang J, Liu Z, et al. Hybrid contextual semantic network for accurate segmentation and detection of small-size stroke lesions from MRI. IEEE J Biomed Health Inform, 2023, 27(8): 4062-4073.
22.	Zhang B, Huang L, Wang J, et al. Semi-supervised fuzzy C means based on membership integration mechanism and its application in brain infarction lesion segmentation in DWI images. J Intell Fuzzy Syst, 2024, 46(1): 2713-2726.
23.	Li T, An X, Di Y, et al. SrSNet: accurate segmentation of stroke lesions by a two-stage segmentation framework with asymmetry information. Expert Syst Appl, 2024, 255: 124329.
24.	Zhang K, Zhu Y, Li H, et al. MDANet: multimodal difference aware network for brain stroke segmentation. Biomed Signal Process Control, 2024, 95: 106383.
25.	Li H, Wu J, Zhang Y, et al. SFMANet: a spatial-frequency multi-scale attention network for stroke lesion segmentation. Sci Rep, 2025, 15(1): 24560.
26.	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need//Advances in Neural Information Processing Systems. Long Beach: Curran Associates Inc, 2017: 5998-6008.
27.	Hatamizadeh A, Nath V, Tang Y, et al. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images//International MICCAI Brainlesion Workshop. Cham: Springer, 2021: 272-284.
28.	Zhang Y, Wu J, Liu Y, et al. MI-UNet: multi-inputs UNet incorporating brain parcellation for stroke lesion segmentation from T1-weighted magnetic resonance images. IEEE J Biomed Health Inform, 2021, 25(2): 526-535.
29.	Bao Q, Mi S, Gang B, et al. MDAN: mirror difference aware network for brain stroke lesion segmentation. IEEE J Biomed Health Inform, 2022, 26(4): 1628-1639.
30.	Huang H, Zheng H, Lin L, et al. Medical image segmentation with deep atlas prior. IEEE Trans Med Imaging, 2021, 40(12): 3519-3530.
31.	張蝶, 黃慧, 馬燕, 等. 基于邊緣信息增強的前列腺MR圖像分割網絡. 中國圖象圖形學報, 2024, 29(3): 755-767.
32.	Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation//2016 Fourth International Conference on 3D Vision (3DV). Stanford: IEEE, 2016: 565-571.
33.	Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell, 2020, 42(2): 318-327.
34.	Hernandez Petzsche M R, De La Rosa E, Hanning U, et al. ISLES 2022: a multi-center magnetic resonance imaging stroke lesion segmentation dataset. Sci Data, 2022, 9(1): 762.
35.	Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.
36.	Xiao X, Lian S, Luo Z, et al. Weighted Res-Unet for high-quality retina vessel segmentation//2018 9th International Conference on Information Technology in Medicine and Education (ITME). Hangzhou: IEEE, 2018: 327-331.
37.	Hatamizadeh A, Tang Y, Nath V, et al. Unetr: transformers for 3d medical image segmentation//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa: IEEE, 2022: 574-584.
38.	Hu J, Shen L, Sun G. Squeeze-and-excitation networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7132-7141.
39.	Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module//Proceedings of the European Conference on Computer Vision (ECCV). Munich: Springer, 2018: 3-19.
40.	Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation. arXiv, 2017: 1706.05587.

1. 毛天馳, 李楊, 李明, 等. 基于深度學習的急性缺血性腦卒中病灶分割與檢測綜述. 計算機系統應用, 2025, 34(1): 11-25.
2. 王隴德, 彭斌, 張鴻祺, 等. 《中國腦卒中防治報告2020》概要. 中國腦血管病雜志, 2022, 19(2): 136-144.
3. 劉樂, 余超, 廖逸文, 等. 1990-2019年中國缺血性腦卒中疾病負擔變化分析. 中國循證醫學雜志, 2022, 22(9): 993-998.
4. 中國卒中學會, 中國卒中學會神經介入分會, 中華預防醫學會卒中預防與控制專業委員會介入學組. 急性缺血性卒中血管內治療影像評估中國專家共識. 中國卒中雜志, 2017, 12(11): 1041-1056.
5. 霍曉川, 高峰. 急性缺血性卒中血管內治療中國指南2023. 中國卒中雜志, 2023, 18(6): 684-711.
6. Miceli G, Rizzo G, Basso M G, et al. Artificial intelligence in symptomatic carotid plaque detection: a narrative review. Appl Sci, 2023, 13(7): 4321.
7. 王姍, 趙建華. 基于CT和MRI的影像組學在缺血性腦卒中的研究進展. CT理論與應用研究, 2024, 33(1): 83-89.
8. Fiez J A, Damasio H, Grabowski T J. Lesion segmentation and manual warping to a reference brain: intra‐ and interobserver reliability. Hum Brain Mapp, 2000, 9(4): 192-211.
9. Montaner J, Rovira A, Molina C A, et al. Plasmatic level of neuroinflammatory markers predict the extent of diffusion-weighted image lesions in hyperacute stroke. J Cereb Blood Flow Metab, 2003, 23(12): 1403-1407.
10. Wittsack H J, Ritzl A, Fink G R, et al. MR imaging in acute stroke: diffusion-weighted and perfusion imaging parameters for predicting infarct size. Radiology, 2002, 222(2): 397-403.
11. Pustina D, Coslett H B, Turkeltaub P E, et al. Automated segmentation of chronic stroke lesions using LINDA: lesion identification with neighborhood data analysis. Hum Brain Mapp, 2016, 37(4): 1405-1421.
12. Karthik R, Menaka R, Johnson A, et al. Neuroimaging and deep learning for brain stroke detection: a review of recent advancements and future prospects. Comput Methods Programs Biomed, 2020, 197: 105728.
13. Luo J, Dai P, He Z, et al. Deep learning models for ischemic stroke lesion segmentation in medical images: a survey. Comput Biol Med, 2024, 175: 108509.
14. Zhou Y, Huang W, Dong P, et al. D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation. IEEE/ACM Trans Comput Biol Bioinform, 2021, 18(3): 940-950.
15. Liu L, Kurgan L, Wu F X, et al. Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease. Med Image Anal, 2020, 65: 101791.
16. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
17. Huang B, Tan G, Dou H, et al. Mutual gain adaptive network for segmenting brain stroke lesions. Appl Soft Comput, 2022, 129: 109568.
18. Li L, Ma K, Song Y, et al. TSRL-Net: target-aware supervision residual learning for stroke segmentation. Comput Biol Med, 2023, 159: 106840.
19. Nie X, Liu X, Yang H, et al. Fully automatic identification of post-treatment infarct lesions after endovascular therapy based on non-contrast computed tomography. Neural Comput Appl, 2023, 35(30): 22101-22114.
20. Ashtari P, Sima D M, De Lathauwer L, et al. Factorizer: a scalable interpretable approach to context modeling for medical image segmentation. Med Image Anal, 2023, 84: 102706.
21. Liu L, Chang J, Liu Z, et al. Hybrid contextual semantic network for accurate segmentation and detection of small-size stroke lesions from MRI. IEEE J Biomed Health Inform, 2023, 27(8): 4062-4073.
22. Zhang B, Huang L, Wang J, et al. Semi-supervised fuzzy C means based on membership integration mechanism and its application in brain infarction lesion segmentation in DWI images. J Intell Fuzzy Syst, 2024, 46(1): 2713-2726.
23. Li T, An X, Di Y, et al. SrSNet: accurate segmentation of stroke lesions by a two-stage segmentation framework with asymmetry information. Expert Syst Appl, 2024, 255: 124329.
24. Zhang K, Zhu Y, Li H, et al. MDANet: multimodal difference aware network for brain stroke segmentation. Biomed Signal Process Control, 2024, 95: 106383.
25. Li H, Wu J, Zhang Y, et al. SFMANet: a spatial-frequency multi-scale attention network for stroke lesion segmentation. Sci Rep, 2025, 15(1): 24560.
26. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need//Advances in Neural Information Processing Systems. Long Beach: Curran Associates Inc, 2017: 5998-6008.
27. Hatamizadeh A, Nath V, Tang Y, et al. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images//International MICCAI Brainlesion Workshop. Cham: Springer, 2021: 272-284.
28. Zhang Y, Wu J, Liu Y, et al. MI-UNet: multi-inputs UNet incorporating brain parcellation for stroke lesion segmentation from T1-weighted magnetic resonance images. IEEE J Biomed Health Inform, 2021, 25(2): 526-535.
29. Bao Q, Mi S, Gang B, et al. MDAN: mirror difference aware network for brain stroke lesion segmentation. IEEE J Biomed Health Inform, 2022, 26(4): 1628-1639.
30. Huang H, Zheng H, Lin L, et al. Medical image segmentation with deep atlas prior. IEEE Trans Med Imaging, 2021, 40(12): 3519-3530.
31. 張蝶, 黃慧, 馬燕, 等. 基于邊緣信息增強的前列腺MR圖像分割網絡. 中國圖象圖形學報, 2024, 29(3): 755-767.
32. Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation//2016 Fourth International Conference on 3D Vision (3DV). Stanford: IEEE, 2016: 565-571.
33. Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell, 2020, 42(2): 318-327.
34. Hernandez Petzsche M R, De La Rosa E, Hanning U, et al. ISLES 2022: a multi-center magnetic resonance imaging stroke lesion segmentation dataset. Sci Data, 2022, 9(1): 762.
35. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.
36. Xiao X, Lian S, Luo Z, et al. Weighted Res-Unet for high-quality retina vessel segmentation//2018 9th International Conference on Information Technology in Medicine and Education (ITME). Hangzhou: IEEE, 2018: 327-331.
37. Hatamizadeh A, Tang Y, Nath V, et al. Unetr: transformers for 3d medical image segmentation//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa: IEEE, 2022: 574-584.
38. Hu J, Shen L, Sun G. Squeeze-and-excitation networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7132-7141.
39. Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module//Proceedings of the European Conference on Computer Vision (ECCV). Munich: Springer, 2018: 3-19.
40. Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation. arXiv, 2017: 1706.05587.

Journal of Biomedical Engineering

Prior knowledge-guided and border-focused segmentation of ischemic stroke lesions

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