自动分割医学影像的有用区域与需匿名区域，提升自监督学习效率与隐私安全。
A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data
- 双分支网络分别定位图像有效区域和需匿名区域。
- 前景分割Dice超99.5，匿名区域分割平均达98.5以上。
- 开源工具包支持CT/MRI自监督学习，兼顾隐私与算力效率。
本研究提出一个开源工具包，解决3D医学影像自监督学习（SSL）预处理中的关键挑战，聚焦数据隐私与计算效率。工具包包含两个核心组件：一个分割网络用于精准划定前景区域，优化数据采样以减少训练时间；另一个分割网络用于识别需匿名区域，防止重建类自监督方法中出现错误监督。实验结果表明，该工具包具备高度鲁棒性，所有匿名化方法下平均Dice分数均超过98.5，前景分割任务更突破99.5，充分验证其在支持CT与MRI影像自监督学习中的有效性。代码与权重已公开于https://github.com/MIC-DKFZ/Foreground-and-Anonymization-Area-Segmentation。
原文摘要 · Abstract (English)
This study presents an open-source toolkit to address critical challenges in preprocessing data for self-supervised learning (SSL) for 3D medical imaging, focusing on data privacy and computational efficiency. The toolkit comprises two main components: a segmentation network that delineates foreground regions to optimize data sampling and thus reduce training time, and a segmentation network that identifies anonymized regions, preventing erroneous supervision in reconstruction-based SSL methods. Experimental results demonstrate high robustness, with mean Dice scores exceeding 98.5 across all anonymization methods and surpassing 99.5 for foreground segmentation tasks, highlighting the efficacy of the toolkit in supporting SSL applications in 3D medical imaging for both CT and MRI images. The weights and code is available at https://github.com/MIC-DKFZ/Foreground-and-Anonymization-Area-Segmentation.
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