MDPNet: a multi-scale difference perception network for esophageal cancer segmentation in CT images.

Fei Qi, Qian Ye, Chang Xu, Jianlin Wang, Liugang Gao

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Article in BMC medical imaging, 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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5 authors.

Fei QiCenter of Medical Physics, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, 213003, China.

Qian YeCollege of Information Science and Engineering, Hohai University, Changzhou, Jiangsu, 213200, China.

Chang XuInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 201210, China.

Jianlin WangCenter of Medical Physics, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, 213003, China.

Liugang GaoCenter of Medical Physics, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, 213003, China. gaoliugang@126.com.

Funding

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6 · The paper itself

Abstract

Accurate segmentation of esophageal cancers in CT images is crucial for disease treatment planning but remains difficult due to variable tumor morphology, low contrast with surrounding tissues, and blurred boundaries. We propose MDPNet, a Multi-scale Difference Perception Network for accurate esophageal cancer segmentation in CT images. MDPNet integrates three key modules, a Dynamic Feature Enhancement (DFE) strategy for global and local context fusion, a Cross-level Difference Modeling (CDM) module to highlight foreground-background differences, and a Multi-stage Foreground Enhancement (MFE) mechanism for progressive boundary refinement. Experiments on the self-built ECD 2D dataset and an external test set show that MDPNet achieves the best performance among state-of-the-art methods, with Dice coefficients of 0.82 and 0.78, respectively. MDPNet effectively improves segmentation accuracy and generalization, demonstrating preliminary generalization capability on our multi-center test sets, suggesting its potential as a decision-support tool.

Indexed as

Esophageal NeoplasmsImage Processing, Computer-AssistedNeural Networks, ComputerRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAlgorithmsHumansCT imagesDifference perceptionEsophageal cancer segmentationMedical image segmentationMulti-scale features

Identifiers

PMID41703480

DOI10.1186/s12880-026-02213-6

PMCPMC13015133

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