Automated body organ segmentation and volumetry for 3D motion-corrected T2-weighted fetal body MRI: a pilot pipeline.

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Abstract

Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. The protocol was used as a basis for training of a neural network for automated multi-label segmentation based on manual segmentations and semi-supervised training. The deep learning pipeline showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time in comparison to conventional manual segmentation. The general feasibility of the proposed pipeline was assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22-38 weeks gestational age range. In addition, the results of comparison between 60 normal and 12 fetal growth restriction datasets revealed significant differences in organ volumes.