Evidence map›Paper›PMID 42415316›Full record

ArticleJournal of applied clinical medical physics2026

FS-Mamba: Feature-wise scanning Mamba UNet for automatic image segmentation in liver tumor radiotherapy.

Peijun Yin, Xueren Zhang, Xin Liu, Zekun Jiang, Qingtao Qiu, Yong Yin, Zhenjiang Li

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In one paragraph

Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Peijun YinDepartment of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Xueren ZhangSchool of Science, Sun Yat-sen University, Shenzhen, China.
Xin LiuDepartment of Gynecological Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, China.
Zekun JiangWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Qingtao QiuDepartment of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Yong YinDepartment of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Zhenjiang LiDepartment of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.

Funding

National Natural Science Foundation of China 12475343
6 · The paper itself

Abstract

backgroundMedical 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.

methodsWe propose FS-Mamba, a U-shaped encoder

resultsFS-Mamba achieved mean DSC/HD95 of 92.81%/10.74 on CT-ORG, 81.28%/14.32 on Synapse

conclusionsFS-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.

Indexed as

AlgorithmsImage Processing, Computer-AssistedLiver NeoplasmsRadiotherapy Planning, Computer-AssistedTomography, X-Ray ComputedHumansOrgans at RiskRadiotherapy DosageRadiotherapy, Intensity-Modulateddeep learningfrequency domain featuresliver cancer radiotherapyMambamedical image segmentationonline adaptive radiotherapystate space model

Identifiers

PMID42415316
PMCPMC13341962

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.