LiNUS: Lightweight Automatic Segmentation of Deep Brain Nuclei for Real-Time DBS Surgery
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866918298743996416 |
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| author | Zhang, Shuo Wang, Zihua He, Changgeng Hu, Chunhua |
| author_facet | Zhang, Shuo Wang, Zihua He, Changgeng Hu, Chunhua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14793 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LiNUS: Lightweight Automatic Segmentation of Deep Brain Nuclei for Real-Time DBS Surgery Zhang, Shuo Wang, Zihua He, Changgeng Hu, Chunhua Image and Video Processing 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. |
| title | LiNUS: Lightweight Automatic Segmentation of Deep Brain Nuclei for Real-Time DBS Surgery |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2601.14793 |