MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset
Wu, Zhaodong Zhao, Qiaochu Hu, Ming Li, Yulong Xue, Haochen Jiang, Zhengyong Stefanidis, Angelos Wang, Qiufeng Razzak, Imran Ge, Zongyuan
Wu, Zhaodong
Zhao, Qiaochu
Hu, Ming
Li, Yulong
Xue, Haochen
Jiang, Zhengyong
Stefanidis, Angelos
Wang, Qiufeng
Razzak, Imran
Ge, Zongyuan
Supervisor
Department
Computational Biology
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Journal Issue
Abstract
With the significantly increasing incidence and prevalence of abdominal diseases, there is a need to embrace greater use of new innovations and technology for the diagnosis and treatment of patients. Although deep-learning methods have notably been developed to assist radiologists in diagnosing abdominal diseases, existing models have the restricted ability to segment common lesions in the abdomen due to missing annotations for typical abdominal pathologies in their training datasets. To address the limitation, we introduce MSWAL, the first 3D Multi-class Segmentation of the Whole Abdominal Lesions dataset, which broadens the coverage of various common lesion types, such as gallstones, kidney stones, liver tumors, kidney tumors, pancreatic cancer, liver cysts, and kidney cysts. With CT scans collected from 694 patients (191,417 slices) of different genders across various scanning phases, MSWAL demonstrates strong robustness and generalizability. The transfer learning experiment from MSWAL to two public datasets, LiTS and KiTS, effectively demonstrates consistent improvements, with Dice Similarity Coefficient (DSC) increase of 3.00% for liver tumors and 0.89% for kidney tumors, demonstrating that the comprehensive annotations and diverse lesion types in MSWAL facilitate effective learning across different domains and data distributions. Furthermore, we propose Inception nnU-Net, a novel segmentation framework that effectively integrates an Inception module with the nnU-Net architecture to extract information from different receptive fields, achieving significant enhancement in both voxel-level DSC and region-level F1 compared to the cutting-edge public algorithms on MSWAL. Our dataset and the code are publicly released at https://github.com/tiuxuxsh76075/MSWAL-.
Citation
Z. Wu et al., “MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset,” LNCS, vol. 15961, pp. 378–388, 2026, doi: 10.1007/978-3-032-04937-7_36
Source
Medical Image Computing and Computer Assisted Intervention
Conference
28 MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
Keywords
Subjects
Source
28 MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
Publisher
Springer Nature