MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset
Journal:
arXiv
Published Date:
Mar 17, 2025
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 will be released after
being accepted, and the code is publicly released at
https://github.com/tiuxuxsh76075/MSWAL-.
