SliceMamba with Neural Architecture Search for Medical Image Segmentation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912567573610496 |
|---|---|
| author | Fan, Chao Yu, Hongyuan Huang, Yan Wang, Liang Yang, Zhenghan Jia, Xibin |
| author_facet | Fan, Chao Yu, Hongyuan Huang, Yan Wang, Liang Yang, Zhenghan Jia, Xibin |
| contents | Despite the progress made in Mamba-based medical image segmentation models, existing methods utilizing unidirectional or multi-directional feature scanning mechanisms struggle to effectively capture dependencies between neighboring positions, limiting the discriminant representation learning of local features. These local features are crucial for medical image segmentation as they provide critical structural information about lesions and organs. To address this limitation, we propose SliceMamba, a simple and effective locally sensitive Mamba-based medical image segmentation model. SliceMamba includes an efficient Bidirectional Slice Scan module (BSS), which performs bidirectional feature slicing and employs varied scanning mechanisms for sliced features with distinct shapes. This design ensures that spatially adjacent features remain close in the scanning sequence, thereby improving segmentation performance. Additionally, to fit the varying sizes and shapes of lesions and organs, we further introduce an Adaptive Slice Search method to automatically determine the optimal feature slice method based on the characteristics of the target data. Extensive experiments on two skin lesion datasets (ISIC2017 and ISIC2018), two polyp segmentation (Kvasir and ClinicDB) datasets, and one multi-organ segmentation dataset (Synapse) validate the effectiveness of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08481 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SliceMamba with Neural Architecture Search for Medical Image Segmentation Fan, Chao Yu, Hongyuan Huang, Yan Wang, Liang Yang, Zhenghan Jia, Xibin Image and Video Processing Computer Vision and Pattern Recognition Despite the progress made in Mamba-based medical image segmentation models, existing methods utilizing unidirectional or multi-directional feature scanning mechanisms struggle to effectively capture dependencies between neighboring positions, limiting the discriminant representation learning of local features. These local features are crucial for medical image segmentation as they provide critical structural information about lesions and organs. To address this limitation, we propose SliceMamba, a simple and effective locally sensitive Mamba-based medical image segmentation model. SliceMamba includes an efficient Bidirectional Slice Scan module (BSS), which performs bidirectional feature slicing and employs varied scanning mechanisms for sliced features with distinct shapes. This design ensures that spatially adjacent features remain close in the scanning sequence, thereby improving segmentation performance. Additionally, to fit the varying sizes and shapes of lesions and organs, we further introduce an Adaptive Slice Search method to automatically determine the optimal feature slice method based on the characteristics of the target data. Extensive experiments on two skin lesion datasets (ISIC2017 and ISIC2018), two polyp segmentation (Kvasir and ClinicDB) datasets, and one multi-organ segmentation dataset (Synapse) validate the effectiveness of our method. |
| title | SliceMamba with Neural Architecture Search for Medical Image Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.08481 |