轻量级模型实现脑深部核团实时自动分割，提升手术精准度
LiNUS: Lightweight Automatic Segmentation of Deep Brain Nuclei for Real-Time DBS Surgery
- 改进U-Net结构，融合谱归一化与多尺度特征
- 单次推理仅需0.05秒，Dice系数达0.679
- 专为临床实时应用设计，适合神经外科医生使用
本文提出LiNUS，一种轻量级深度学习框架，用于在深部脑刺激（DBS）手术中自动分割丘脑底核（STN）。针对MRI数据中小目标体积和类别不平衡的挑战，LiNUS通过引入谱归一化约束、双线性插值上采样及多尺度特征融合机制，对U-Net架构进行优化。在清华大学DBS数据集（TT14）上的实验表明，LiNUS单例推理时间仅为0.05秒，Dice系数达0.679，显著优于传统手动与配准方法。在高分辨率数据上的进一步验证显示模型鲁棒性，Dice分数达0.89。同时开发了专用图形用户界面（GUI），以支持实时临床应用。
原文摘要 · Abstract (English)
This paper proposes LiNUS, a lightweight deep learning framework for the automatic segmentation of the Subthalamic Nucleus (STN) in Deep Brain Stimulation (DBS) surgery. Addressing the challenges of small target volume and class imbalance in MRI data, LiNUS improves upon the U-Net architecture by introducing spectral normalization constraints, bilinear interpolation upsampling, and a multi-scale feature fusion mechanism. Experimental results on the Tsinghua DBS dataset (TT14) demonstrate that LiNUS achieves a Dice coefficient of 0.679 with an inference time of only 0.05 seconds per subject, significantly outperforming traditional manual and registration-based methods. Further validation on high-resolution data confirms the model's robustness, achieving a Dice score of 0.89. A dedicated Graphical User Interface (GUI) was also developed to facilitate real-time clinical application.
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