轻量级3D医学图像分割模型，用锚点引导实现高精度与低资源消耗。
LightMedSeg: Lightweight 3D Medical Image Segmentation with Learned Spatial Anchors
- 基于UNet结构，引入解剖先验和自适应上下文建模。
- 仅0.48M参数、14.64 GFLOPs，Dice分数接近大模型基准。
- 适合临床部署，尤其在数据少、算力受限场景下使用。
精准高效的3D医学图像分割对临床AI至关重要，但模型需在内存、延迟和数据稀缺的严苛条件下保持可靠性。基于Transformer的方法虽准确度高，却存在参数过多、计算量大、泛化能力弱的问题。本文提出LightMedSeg，一种模块化UNet式分割架构，融合解剖先验与自适应上下文建模。锚点条件的FiLM调制实现解剖感知特征校准，局部结构先验模块与纹理感知路由动态分配表示能力至边界密集区域。通过鬼影卷积和深度可分离卷积减少计算冗余，多尺度特征通过带锚点相对空间位置偏置的可学习跳跃路由自适应融合。尽管仅需0.48M参数和14.64 GFLOPs，LightMedSeg的分割精度仅比重型Transformer基线低数个Dice点。因此，LightMedSeg是可部署且数据高效的3D医学图像分割方案。代码将在录用后公开。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。