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arXiv · 2603.07228

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

Kavyansh Tyagi·Vishwas Rathi·Puneet Goyal
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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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Kavyansh Tyagi, Vishwas Rathi, Puneet Goyal. 2026-03-07. LightMedSeg: Lightweight 3D Medical Image Segmentation with Learned Spatial Anchors. https://arxiv.org/abs/2603.07228

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