MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging
Abstract Background Single-photon emission computed tomography (SPECT) plays a crucial role in detecting bone metastases from lung cancer. However, its low spatial resolution and lesion similarity to benign structures present significant challenges for accurate segmentation, especially for lesions o...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | EJNMMI Physics |
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| Online Access: | https://doi.org/10.1186/s40658-025-00785-w |
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| author | Yusheng Wu Qiang Lin Yang He XianWu Zeng Yongchun Cao ZhengXing Man Caihong Liu Yusheng Hao Zhengqi Cai Jinshui Ji Xiaodi Huang |
| author_facet | Yusheng Wu Qiang Lin Yang He XianWu Zeng Yongchun Cao ZhengXing Man Caihong Liu Yusheng Hao Zhengqi Cai Jinshui Ji Xiaodi Huang |
| author_sort | Yusheng Wu |
| collection | DOAJ |
| description | Abstract Background Single-photon emission computed tomography (SPECT) plays a crucial role in detecting bone metastases from lung cancer. However, its low spatial resolution and lesion similarity to benign structures present significant challenges for accurate segmentation, especially for lesions of varying sizes. Methods We propose a deep learning-based segmentation framework that integrates conditional adversarial learning with a multi-scale feature extraction generator. The generator employs cascade dilated convolutions, multi-scale modules, and deep supervision, while the discriminator utilizes multi-scale L1 loss computed on image-mask pairs to guide segmentation learning. Results The proposed model was evaluated on a dataset of 286 clinically annotated SPECT scintigrams. It achieved a Dice Similarity Coefficient (DSC) of 0.6671, precision of 0.7228, and recall of 0.6196 — outperforming both classical and recent adversarial segmentation models in multi-scale lesion detection, especially for small and clustered lesions. Conclusion Our results demonstrate that the integration of multi-scale feature learning with adversarial supervision significantly improves the segmentation of bone metastasis in SPECT imaging. This approach shows potential for clinical decision support in the management of lung cancer. |
| format | Article |
| id | doaj-art-8035f63cf06847c8825ffaa18d52c49e |
| institution | DOAJ |
| issn | 2197-7364 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EJNMMI Physics |
| spelling | doaj-art-8035f63cf06847c8825ffaa18d52c49e2025-08-20T03:06:06ZengSpringerOpenEJNMMI Physics2197-73642025-07-0112112110.1186/s40658-025-00785-wMSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imagingYusheng Wu0Qiang Lin1Yang He2XianWu Zeng3Yongchun Cao4ZhengXing Man5Caihong Liu6Yusheng Hao7Zhengqi Cai8Jinshui Ji9Xiaodi Huang10Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu UniversityKey Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu UniversitySchool of Mathematics and Computer Science, Northwest Minzu UniversityDepartment of Nuclear Medicine, Gansu Provincial Cancer HospitalKey Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu UniversityKey Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu UniversityKey Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu UniversityKey Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu UniversityKey Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu UniversityKey Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu UniversitySchool of Computing, Mathematics and Engineering, Charles Sturt UniversityAbstract Background Single-photon emission computed tomography (SPECT) plays a crucial role in detecting bone metastases from lung cancer. However, its low spatial resolution and lesion similarity to benign structures present significant challenges for accurate segmentation, especially for lesions of varying sizes. Methods We propose a deep learning-based segmentation framework that integrates conditional adversarial learning with a multi-scale feature extraction generator. The generator employs cascade dilated convolutions, multi-scale modules, and deep supervision, while the discriminator utilizes multi-scale L1 loss computed on image-mask pairs to guide segmentation learning. Results The proposed model was evaluated on a dataset of 286 clinically annotated SPECT scintigrams. It achieved a Dice Similarity Coefficient (DSC) of 0.6671, precision of 0.7228, and recall of 0.6196 — outperforming both classical and recent adversarial segmentation models in multi-scale lesion detection, especially for small and clustered lesions. Conclusion Our results demonstrate that the integration of multi-scale feature learning with adversarial supervision significantly improves the segmentation of bone metastasis in SPECT imaging. This approach shows potential for clinical decision support in the management of lung cancer.https://doi.org/10.1186/s40658-025-00785-wSPECT imageBone metastasisMultiscale segmentationAdversarial learning |
| spellingShingle | Yusheng Wu Qiang Lin Yang He XianWu Zeng Yongchun Cao ZhengXing Man Caihong Liu Yusheng Hao Zhengqi Cai Jinshui Ji Xiaodi Huang MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging EJNMMI Physics SPECT image Bone metastasis Multiscale segmentation Adversarial learning |
| title | MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging |
| title_full | MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging |
| title_fullStr | MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging |
| title_full_unstemmed | MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging |
| title_short | MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging |
| title_sort | msa net a multi scale and adversarial learning network for segmenting bone metastases in low resolution spect imaging |
| topic | SPECT image Bone metastasis Multiscale segmentation Adversarial learning |
| url | https://doi.org/10.1186/s40658-025-00785-w |
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