LightMedSeg: Lightweight 3D Medical Image Segmentation with Learned Spatial Anchors

• URL: http://arxiv.org/abs/2603.07228v1
• Date: Sat, 07 Mar 2026 14:20:59 GMT
• Title: LightMedSeg: Lightweight 3D Medical Image Segmentation with Learned Spatial Anchors
• Abstract summary: Transformer-based methods achieve strong accuracy but suffer from excessive parameters, high FLOPs, and limited generalization.<n>We propose LightMedSeg, a modular UNet-style segmentation architecture that integrates anatomical priors with adaptive context modeling.<n>LightMedSeg achieves segmentation accuracy within a few Dice points of heavy transformer baselines.
• Score: 6.372261626436676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and efficient 3D medical image segmentation is essential for clinical AI, where models must remain reliable under stringent memory, latency, and data availability constraints. Transformer-based methods achieve strong accuracy but suffer from excessive parameters, high FLOPs, and limited generalization. We propose LightMedSeg, a modular UNet-style segmentation architecture that integrates anatomical priors with adaptive context modeling. Anchor-conditioned FiLM modulation enables anatomy-aware feature calibration, while a local structural prior module and texture-aware routing dynamically allocate representational capacity to boundary-rich regions. Computational redundancy is minimized through ghost and depthwise convolutions, and multi-scale features are adaptively fused via a learned skip router with anchor-relative spatial position bias. Despite requiring only 0.48M parameters and 14.64~GFLOPs, LightMedSeg achieves segmentation accuracy within a few Dice points of heavy transformer baselines. Therefore, LightMedSeg is a deployable and data-efficient solution for 3D medical image segmentation. Code will be released publicly upon acceptance.

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