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bioRxiv · 10.1101/2022.08.26.505447

DEEP LEARNING ENABLED MULTI-ORGAN SEGMENTATION OF MOUSE EMBRYOS

Rolfe, S. M.·Maga, A. M.
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Abstract

The International Mouse Phenotyping Consortium (IMPC) has generated a large repository of 3D imaging data from mouse embryos, providing a rich resource for investigating phenotype/genotype interactions. While the data is freely available, the computing resources and human effort required to segment these images for analysis of individual structures can create a significant hurdle for research. In this paper, we present an open source, deep learning-enabled tool, Mouse Embryo Multi-Organ Segmentation (MEMOS), that estimates a segmentation of 50 anatomical structures with a support for manually reviewing, editing, and analyzing the estimated segmentation in a single application. MEMOS is implemented as an extension on the 3D Slicer platform and is designed to be accessible to researchers without coding experience. We validate the performance of MEMOS-generated segmentations through comparison to state-of-the-art atlas-based segmentation and quantification of previously reported anatomical abnormalities in a CBX4 knockout strain. SUMMARY STATEMENTWe present a new open source, deep learning-enabled tool, Mouse Embryo Multi-Organ Segmentation (MEMOS), to estimate the segmentation of 50 anatomical structures from microCT scans of embryonic mice.

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Rolfe, S. M., Maga, A. M.. 2022-08-26. DEEP LEARNING ENABLED MULTI-ORGAN SEGMENTATION OF MOUSE EMBRYOS. https://doi.org/10.1101/2022.08.26.505447

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