基于局部上下文引导特征深度融合的轻量级医学图像分割方法

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中图分类号：TN209 文献标志码：A 文章编号：1671-6841（2026）01-0065-07

DOI：10. 13705/j. issn.1671-6841. 2024091

Abstract： For most of the existing deep learning-based medical image segmentation，a large amount of training data were used to improve the detection network to achieve excellnt detection performance. These methods needed a large number of the models and parameter，resulting in poor real-time detection performance.To addressthis，a local context guided feature deep fusion lightweight medical segmentation network（LCGML-net） was proposed.The main idea of LCGML-net was to reduce the number of parameters required for model fiting by accurate feature selection and feature fusion， thus achieving smaller model while maintaining detection accuracy. LCGML-net enriched the feature representation accurate precision segmentation of the target by extracting dense multi-level and multi-scale local context features of the target in the feature extraction stage and the feature mapping stage，respectively. Extensive experiments were conducted on multiple medical segmentation benchmark datasets， including STARE， CHASEDB1，and KITS19. The results demonstrated that compared to other advanced methods，the proposed LCGML-net exhibited the best detection performance with the smallst model parameters.

Key words：medical image segmentation； neural network；local context feature； deep fusion

0 引言

图像分割在医学领域起着至关重要的作用，为医务人员提供了可靠的诊断依据[1]。（剩余11659字）