Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain in-utero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

翻译：扩散加权磁共振成像（dMRI）正日益广泛地应用于研究宫内胎儿脑部的正常与异常发育。近期研究表明，dMRI能为胎儿阶段的神经发育过程提供宝贵的见解。然而，由于数据质量较低且脑部发育迅速，对胎儿dMRI数据进行可靠分析需要专门的计算方法，而目前此类方法尚属空白。缺乏快速、准确且可重复的自动化数据分析方法，严重限制了我们在医学与科学应用中挖掘胎儿脑部dMRI潜力的能力。本研究开发并验证了一个统一的计算框架，旨在实现：（1）将脑组织分割为白质、皮质/皮质下灰质及脑脊液；（2）分割31条不同的白质纤维束；（3）将大脑皮质分区，并将深部灰质核团与白质结构划分为96个具有解剖学意义的区域。我们采用手动、半自动及自动相结合的方法对97例胎儿脑部数据进行了标注。基于这些标注数据，我们开发并验证了一种多任务深度学习方法以执行上述三项计算任务。评估结果表明，新方法能准确完成所有三项任务，在组织分割、白质纤维束分割及脑区分割任务中分别达到0.865、0.825和0.819的平均Dice相似系数。该方法能显著提升胎儿脑部纤维追踪、纤维束特异性分析及结构连接评估的水平，有望极大推动胎儿神经影像学领域的发展。