M2FNet: multi-modality multi-level fusion network for segmentation of acute and sub-acute ischemic stroke
Abstract Ischemic stroke, a leading cause of death and disability, necessitates accurate detection and automatic segmentation of lesions. While diffusion weight imaging is crucial, its single modality limits the detection of subtle lesions and artifacts. To address this, we propose a multi-modality,...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-04-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01861-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850154609666949120 |
|---|---|
| author | Shannan Chen Xuanhe Zhao Yang Duan Ronghui Ju Peizhuo Zang Shouliang Qi |
| author_facet | Shannan Chen Xuanhe Zhao Yang Duan Ronghui Ju Peizhuo Zang Shouliang Qi |
| author_sort | Shannan Chen |
| collection | DOAJ |
| description | Abstract Ischemic stroke, a leading cause of death and disability, necessitates accurate detection and automatic segmentation of lesions. While diffusion weight imaging is crucial, its single modality limits the detection of subtle lesions and artifacts. To address this, we propose a multi-modality, multi-level fusion network (M2FNet) that aggregates salient features from different modalities across various levels. Our method uses a multi-modal independent encoder to extract modality-specific features from images of different modalities, thereby preserving key details and ensuring rich features. In order to suppress noise while ensuring the best preservation of modality-specific information, we effectively integrate features of different modalities through a cross-modal encoder fusion module. In addition, a cross-modal decoder fusion module and a multi-modality joint loss are designed to further improve the fusion of high-level and low-level features in the up-sampling stage, dynamically utilizing complementary information from multiple modalities to improve segmentation accuracy. To verify the effectiveness of our proposed method, M2FNet was validated on two public magnetic resonance imaging ischemic stroke lesion segmentation benchmark datasets. Whether single or multi-modality, M2FNet performed better than ten other baseline methods. This highlights the effectiveness of M2FNet in multi-modality segmentation of ischemic stroke lesions, making it a promising and powerful quantitative analysis tool for rapid and accurate diagnostic support. The codes of M2FNet are available at https://github.com/ShannanChen/MMFNet . |
| format | Article |
| id | doaj-art-6d6c8ff23899460b965e70c7b01a0a8e |
| institution | OA Journals |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-6d6c8ff23899460b965e70c7b01a0a8e2025-08-20T02:25:16ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-04-0111612410.1007/s40747-025-01861-5M2FNet: multi-modality multi-level fusion network for segmentation of acute and sub-acute ischemic strokeShannan Chen0Xuanhe Zhao1Yang Duan2Ronghui Ju3Peizhuo Zang4Shouliang Qi5College of Medicine and Biological Information Engineering, Northeastern UniversityCollege of Medicine and Biological Information Engineering, Northeastern UniversityDepartment of Radiology, General Hospital of Northern Theater CommandDepartment of Radiology, The People’s Hospital of Liaoning ProvinceDepartment of Cerebrovascular Disease Treatment Center, The People’s Hospital of Liaoning ProvinceCollege of Medicine and Biological Information Engineering, Northeastern UniversityAbstract Ischemic stroke, a leading cause of death and disability, necessitates accurate detection and automatic segmentation of lesions. While diffusion weight imaging is crucial, its single modality limits the detection of subtle lesions and artifacts. To address this, we propose a multi-modality, multi-level fusion network (M2FNet) that aggregates salient features from different modalities across various levels. Our method uses a multi-modal independent encoder to extract modality-specific features from images of different modalities, thereby preserving key details and ensuring rich features. In order to suppress noise while ensuring the best preservation of modality-specific information, we effectively integrate features of different modalities through a cross-modal encoder fusion module. In addition, a cross-modal decoder fusion module and a multi-modality joint loss are designed to further improve the fusion of high-level and low-level features in the up-sampling stage, dynamically utilizing complementary information from multiple modalities to improve segmentation accuracy. To verify the effectiveness of our proposed method, M2FNet was validated on two public magnetic resonance imaging ischemic stroke lesion segmentation benchmark datasets. Whether single or multi-modality, M2FNet performed better than ten other baseline methods. This highlights the effectiveness of M2FNet in multi-modality segmentation of ischemic stroke lesions, making it a promising and powerful quantitative analysis tool for rapid and accurate diagnostic support. The codes of M2FNet are available at https://github.com/ShannanChen/MMFNet .https://doi.org/10.1007/s40747-025-01861-5Ischemic strokeMagnetic resonance imagingMulti-modality fusionDeep learningSegmentation |
| spellingShingle | Shannan Chen Xuanhe Zhao Yang Duan Ronghui Ju Peizhuo Zang Shouliang Qi M2FNet: multi-modality multi-level fusion network for segmentation of acute and sub-acute ischemic stroke Complex & Intelligent Systems Ischemic stroke Magnetic resonance imaging Multi-modality fusion Deep learning Segmentation |
| title | M2FNet: multi-modality multi-level fusion network for segmentation of acute and sub-acute ischemic stroke |
| title_full | M2FNet: multi-modality multi-level fusion network for segmentation of acute and sub-acute ischemic stroke |
| title_fullStr | M2FNet: multi-modality multi-level fusion network for segmentation of acute and sub-acute ischemic stroke |
| title_full_unstemmed | M2FNet: multi-modality multi-level fusion network for segmentation of acute and sub-acute ischemic stroke |
| title_short | M2FNet: multi-modality multi-level fusion network for segmentation of acute and sub-acute ischemic stroke |
| title_sort | m2fnet multi modality multi level fusion network for segmentation of acute and sub acute ischemic stroke |
| topic | Ischemic stroke Magnetic resonance imaging Multi-modality fusion Deep learning Segmentation |
| url | https://doi.org/10.1007/s40747-025-01861-5 |
| work_keys_str_mv | AT shannanchen m2fnetmultimodalitymultilevelfusionnetworkforsegmentationofacuteandsubacuteischemicstroke AT xuanhezhao m2fnetmultimodalitymultilevelfusionnetworkforsegmentationofacuteandsubacuteischemicstroke AT yangduan m2fnetmultimodalitymultilevelfusionnetworkforsegmentationofacuteandsubacuteischemicstroke AT ronghuiju m2fnetmultimodalitymultilevelfusionnetworkforsegmentationofacuteandsubacuteischemicstroke AT peizhuozang m2fnetmultimodalitymultilevelfusionnetworkforsegmentationofacuteandsubacuteischemicstroke AT shouliangqi m2fnetmultimodalitymultilevelfusionnetworkforsegmentationofacuteandsubacuteischemicstroke |