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FS-Mamba: Feature-wise scanning Mamba UNet for automatic image segmentation in liver tumor radiotherapy
Peijun Yin1, Xueren Zhang2, Xin Liu3
1Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Background:
Medical image segmentation is fundamental to radiotherapy planning, yet accurate delineation of organs at risk and tumor targets remain challenging due to anatomical variability and low soft-tissue contrast in CT images.
Purpose:
To develop a lightweight, high-precision automatic segmentation network that meets the dual clinical requirements of accuracy and computational efficiency for online adaptive liver cancer radiotherapy.
Methods:
We propose FS-Mamba, a U-shaped encoder21-decoder network built upon a novel Frequency-Long SSM Block that integrates three innovations: (1) a flip-selective scanning strategy (FS-Scanning) that dynamically reorders feature sequences to prioritize salient information; (2) a frequency-domain modeling branch using Fast Fourier Transform to enhance boundary discrimination; and (3) a long-memory state space model (LSSM) that augments the A-matrix to strengthen historical state retention. The model was validated on three datasets: the public CT-ORG (140 CT volumes, 6 organs) and Synapse (30 cases, 8 organs) benchmarks, and an in-house liver cancer dataset (50 patients, 12 structures including PTV and GTV). Comparisons were made against UNet, nnUNet, TransUNet, Swin-UMamba, and the clinically mainstream TotalSegmentator using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), and subjective physicist scoring.
Results:
FS-Mamba achieved mean DSC/HD95 of 92.81%/10.74 on CT-ORG, 81.28%/14.32 on Synapse30, and 92.68%/10.17 on the liver cancer dataset, outperforming all compared methods with statistical significance (p < 0.05). It received the highest mean subjective clinical score of 4.69/5.0 from 10 blinded medical physicists, approaching the ground-truth reference (4.94/5.0). Despite its leading accuracy, FS-Mamba maintains the smallest model size (20.57 M parameters, 31.94 GFLOPs) and fastest inference (20.16 ms per case), representing a 4.5x parameter reduction and 6.5x speedup compared to TotalSegmentator.
Conclusions:
FS-Mamba provides a clinically viable solution for automated multi-organ and tumor target segmentation that satisfies the stringent time constraints of online adaptive radiotherapy while maintaining expert-level accuracy.
