Ischemic stroke infarct segmentation model based on depthwise separable convolution for multimodal magnetic resonance imaging_Journal of Biomedical Engineering

Authors：

JIN Yidong ¹ , WANG Mengfei ¹ , CHEN Jingjing ² ,  LI Yuehua ^1,3

1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Yangzhi Rehabilitation Hospital Affiliated to Tong Ji University, Shanghai 201619, P. R. China;
3. Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 200233, P. R. China;

Corresponding author：

LI Yuehua, Email: liyuehua312@163.com

Keywords：

Stroke; Infarct segmentation; Depthwise separable convolution; Atrous convolution; Multimodal

DOI：

10.7507/1001-5515.202308001

Video：

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Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of ischemic stroke. Accurate segmentation of the infarct is of great significance for selecting intervention treatment methods and evaluating the prognosis of patients. To address the issue of poor segmentation accuracy of existing methods for multiscale stroke lesions, a novel encoder-decoder architecture network based on depthwise separable convolution is proposed. Firstly, this network replaces the convolutional layer modules of the U-Net with redesigned depthwise separable convolution modules. Secondly, an modified Atrous spatial pyramid pooling (MASPP) is introduced to enlarge the receptive field and enhance the extraction of multiscale features. Thirdly, an attention gate (AG) structure is incorporated at the skip connections of the network to further enhance the segmentation accuracy of multiscale targets. Finally, Experimental evaluations are conducted using the ischemic stroke lesion segmentation 2022 challenge (ISLES2022) dataset. The proposed algorithm in this paper achieves Dice similarity coefficient (DSC), Hausdorff distance (HD), sensitivity (SEN), and precision (PRE) scores of 0.816 5, 3.668 1, 0.889 2, and 0.894 6, respectively, outperforming other mainstream segmentation algorithms. The experimental results demonstrate that the method in this paper effectively improves the segmentation of infarct lesions, and is expected to provide a reliable support for clinical diagnosis and treatment.

Citation： JIN Yidong, WANG Mengfei, CHEN Jingjing, LI Yuehua. Ischemic stroke infarct segmentation model based on depthwise separable convolution for multimodal magnetic resonance imaging. Journal of Biomedical Engineering, 2024, 41(3): 535-543. doi: 10.7507/1001-5515.202308001 Copy

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4.	中国卒中学会, 中国卒中学会神经介入分会, 中华预防医学会卒中预防与控制专业委员会介入学组. 急性缺血性卒中血管内治疗影像评估中国专家共识. 中国卒中杂志, 2017, 12(11): 1041-1056.
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7.	Subbanna N K, Rajashekar D, Cheng B, et al. Stroke lesion segmentation in FLAIR MRI datasets using customized Markov random fields. Frontiers in Neurology, 2019, 10: 541.
8.	Anbumozhi S. Computer aided detection and diagnosis methodology for brain stroke using adaptive neuro fuzzy inference system classifier. International Journal of Imaging Systems and Technology, 2020, 30(1): 196-202.
9.	Mokin M, Primiani C T, Siddiqui A H, et al. ASPECTS (Alberta Stroke Program Early CT Score) measurement using Hounsfield unit values when selecting patients for stroke thrombectomy. Stroke, 2017, 48(6): 1574-1579.
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11.	Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer Cham, 2015: 234-241.
12.	Clèrigues A, Valverde S, Bernal J, et al. Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks. Computers in Biology and Medicine, 2019, 115: 103487.
13.	Soltanpour M, Greiner R, Boulanger P, et al. Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network. Computers in Biology and Medicine, 2021, 137: 104849.
14.	Song L I, Geoffrey K F, Kaijian H E. Bottleneck feature supervised U-net for pixel-wise liver and tumor segmentation. Expert Systems with Applications, 2020, 145: 113131.
15.	Feng X, Tustison N J, Patel S H, et al. Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features. Frontiers in Computational Neuroscience, 2020, 14: 25.
16.	Kumar A, Ghosal P, Kundu S S, et al. A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images. Computer Methods and Programs in Biomedicine, 2022, 226: 107157.
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21.	Cao K, Zhang X. An improved Res-UNet model for tree species classification using airborne high-resolution images. Remote Sensing, 2020, 12(7): 1128.
22.	Karthik R, Gupta U, Jha A, et al. A deep supervised approach for ischemic lesion segmentation from multimodal MRI using fully convolutional network. Applied Soft Computing, 2019, 84: 105685.
23.	Clèrigues A, Valverde S, Bernal J, et al. Acute and sub-acute stroke lesion segmentation from multimodal MRI. Computer Methods and Programs in Biomedicine, 2020, 194: 105521.
24.	Howard A G, Zhu M, Chen B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint, 2017. DOI: 10.48550/arXiv.1704.04861.
25.	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need// Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6000-6010.
26.	Liu Z, Mao H, Wu C Y, et al. A ConvNet for the 2020s. arXiv preprints, 2022. DOI: 10.48550/arXiv. 2201.03545.
27.	Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.
28.	Oktay O, Schlemper J, Folgoc L L, et al. Attention U-Net: Learning Where to Look for the Pancreas. arXiv preprints, 2018. DOI: 10.48550/arXiv.1804.03999.
29.	Hernandez Petzsche M R, de la Rosa E, Hanning U, et al. ISLES 2022: a multi-center magnetic resonance imaging stroke lesion segmentation dataset. Scientific Data, 2022, 9(1): 762.
30.	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). IEEE, 2016: 565-571.
31.	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, IEEE, 2022: 574-584.
32.	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 International Publishing, 2021: 272-284.

1. Feigin V L, Stark B A, Johnson C O, et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol, 2021, 20(10): 795-820.
2. Thayabaranathan T, Kim J, Cadilhac D A, et al. Global stroke statistics 2022. International Journal of Stroke, 2022, 17(9): 946-956.
3. Li Z, Jiang Y, Li H, et al. China’s response to the rising stroke burden. BMJ, 2019, 364: l879.
4. 中国卒中学会, 中国卒中学会神经介入分会, 中华预防医学会卒中预防与控制专业委员会介入学组. 急性缺血性卒中血管内治疗影像评估中国专家共识. 中国卒中杂志, 2017, 12(11): 1041-1056.
5. 中国卒中学会, 中国卒中学会神经介入分会, 中华预防医学会卒中预防与控制专业委员会介入学组. 急性缺血性卒中血管内治疗中国指南2023. 中国卒中杂志, 2023, 18(6): 684-711.
6. de Ruijter J, van Sambeek M, van de Vosse F, et al. Automated 3D geometry segmentation of the healthy and diseased carotid artery in free-hand, probe tracked ultrasound images. Med Phys, 2020, 47(3): 1034-1047.
7. Subbanna N K, Rajashekar D, Cheng B, et al. Stroke lesion segmentation in FLAIR MRI datasets using customized Markov random fields. Frontiers in Neurology, 2019, 10: 541.
8. Anbumozhi S. Computer aided detection and diagnosis methodology for brain stroke using adaptive neuro fuzzy inference system classifier. International Journal of Imaging Systems and Technology, 2020, 30(1): 196-202.
9. Mokin M, Primiani C T, Siddiqui A H, et al. ASPECTS (Alberta Stroke Program Early CT Score) measurement using Hounsfield unit values when selecting patients for stroke thrombectomy. Stroke, 2017, 48(6): 1574-1579.
10. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation//Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, 2015: 3431-3440.
11. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer Cham, 2015: 234-241.
12. Clèrigues A, Valverde S, Bernal J, et al. Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks. Computers in Biology and Medicine, 2019, 115: 103487.
13. Soltanpour M, Greiner R, Boulanger P, et al. Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network. Computers in Biology and Medicine, 2021, 137: 104849.
14. Song L I, Geoffrey K F, Kaijian H E. Bottleneck feature supervised U-net for pixel-wise liver and tumor segmentation. Expert Systems with Applications, 2020, 145: 113131.
15. Feng X, Tustison N J, Patel S H, et al. Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features. Frontiers in Computational Neuroscience, 2020, 14: 25.
16. Kumar A, Ghosal P, Kundu S S, et al. A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images. Computer Methods and Programs in Biomedicine, 2022, 226: 107157.
17. Tomita N, Jiang S, Maeder M E, et al. Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network. Neuroimage Clin, 2020, 27: 102276.
18. 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, IEEE, 2016: 770-778.
19. Drozdzal M, Vorontsov E, Chartrand G, et al. The importance of skip connections in biomedical image segmentation//International Workshop on Deep Learning in Medical Image Analysis, International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. Munich: Springer Cham, 2016: 179-187.
20. Aboudi F, Drissi C, Kraiem T. Efficient U-Net CNN with data augmentation for MRI ischemic stroke brain segmentation//2022 8th International Conference on Control, Decision and Information Technologies (CODIT’22), Istanbul, Turkey: IEEE, 2022: 724-728.
21. Cao K, Zhang X. An improved Res-UNet model for tree species classification using airborne high-resolution images. Remote Sensing, 2020, 12(7): 1128.
22. Karthik R, Gupta U, Jha A, et al. A deep supervised approach for ischemic lesion segmentation from multimodal MRI using fully convolutional network. Applied Soft Computing, 2019, 84: 105685.
23. Clèrigues A, Valverde S, Bernal J, et al. Acute and sub-acute stroke lesion segmentation from multimodal MRI. Computer Methods and Programs in Biomedicine, 2020, 194: 105521.
24. Howard A G, Zhu M, Chen B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint, 2017. DOI: 10.48550/arXiv.1704.04861.
25. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need// Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6000-6010.
26. Liu Z, Mao H, Wu C Y, et al. A ConvNet for the 2020s. arXiv preprints, 2022. DOI: 10.48550/arXiv. 2201.03545.
27. Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.
28. Oktay O, Schlemper J, Folgoc L L, et al. Attention U-Net: Learning Where to Look for the Pancreas. arXiv preprints, 2018. DOI: 10.48550/arXiv.1804.03999.
29. Hernandez Petzsche M R, de la Rosa E, Hanning U, et al. ISLES 2022: a multi-center magnetic resonance imaging stroke lesion segmentation dataset. Scientific Data, 2022, 9(1): 762.
30. 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). IEEE, 2016: 565-571.
31. 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, IEEE, 2022: 574-584.
32. 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 International Publishing, 2021: 272-284.

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Ischemic stroke infarct segmentation model based on depthwise separable convolution for multimodal magnetic resonance imaging

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