用测试时自适应提升胎儿脑部超声分割精度
Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound
- 引入解剖先验的规范图谱，改进测试时自适应方法
- 在多种真实与模拟域偏移下显著提升分割效果
- 适合从事医学影像自动化分析的研究者参考
监测胎儿脑部皮层下区域的生长有助于发现发育异常。手动分割难度大，近年研究显示可借助深度学习实现自动化。然而，将预训练模型应用于未见过的手持超声图像时，由于成像和对齐差异巨大，性能常显著下降。本文首次证明，测试时自适应（TTA）可有效缓解真实与模拟域偏移带来的性能退化。进一步提出一种新方法，通过引入解剖学规范图谱作为先验信息增强TTA。在多种域偏移场景下，对比不同TTA方法，验证了所提方法的优越性，有望推动胎儿脑发育的自动化监测。代码已开源：https://github.com/joshuaomolegan/TTA-for-3D-Fetal-Subcortical-Segmentation。
原文摘要 · Abstract (English)
Monitoring the growth of subcortical regions of the fetal brain in ultrasound (US) images can help identify the presence of abnormal development. Manually segmenting these regions is a challenging task, but recent work has shown that it can be automated using deep learning. However, applying pretrained models to unseen freehand US volumes often leads to a degradation of performance due to the vast differences in acquisition and alignment. In this work, we first demonstrate that test time adaptation (TTA) can be used to improve model performance in the presence of both real and simulated domain shifts. We further propose a novel TTA method by incorporating a normative atlas as a prior for anatomy. In the presence of various types of domain shifts, we benchmark the performance of different TTA methods and demonstrate the improvements brought by our proposed approach, which may further facilitate automated monitoring of fetal brain development. Our code is available at https://github.com/joshuaomolegan/TTA-for-3D-Fetal-Subcortical-Segmentation.
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