Deep learning enabled multi-organ segmentation of mouse embryos.

The International Mouse Phenotyping Consortium (IMPC) has generated a large repository of three-dimensional (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. This article has an associated First Person interview with the first author of the paper.

Defining Multiple Characteristic Raman Bands of α-Amino Acids as Biomarkers for Planetary Missions Using a Statistical Method.

Biomarker molecules, such as amino acids, are key to discovering whether life exists elsewhere in the Solar System. Raman spectroscopy, a technique capable of detecting biomarkers, will be on board future planetary missions including the ExoMars rover. Generally, the position of the strongest band in the spectra of amino acids is reported as the identifying band. However, for an unknown sample, it is desirable to define multiple characteristic bands for molecules to avoid any ambiguous identification. To date, there has been no definition of multiple characteristic bands for amino acids of interest to astrobiology. This study examined L-alanine, L-aspartic acid, L-cysteine, L-glutamine and glycine and defined several Raman bands per molecule for reference as characteristic identifiers. Per amino acid, 240 spectra were recorded and compared using established statistical tests including ANOVA. The number of characteristic bands defined were 10, 12, 12, 14 and 19 for L-alanine (strongest intensity band: 832 cm(-1)), L-aspartic acid (938 cm(-1)), L-cysteine (679 cm(-1)), L-glutamine (1090 cm(-1)) and glycine (875 cm(-1)), respectively. The intensity of bands differed by up to six times when several points on the crystal sample were rotated through 360 °; to reduce this effect when defining characteristic bands for other molecules, we find that spectra should be recorded at a statistically significant number of points per sample to remove the effect of sample rotation. It is crucial that sets of characteristic Raman bands are defined for biomarkers that are targets for future planetary missions to ensure a positive identification can be made.

A new tool for quantifying and characterizing asymmetry in bilaterally paired structures.

This paper introduces a new tool to quantify and characterize asymmetry in bilaterally paired structures. This method uses deformable registration to produce a dense vector field describing the point correspondences between two images of bilaterally paired structures. The deformation vector field properties are clustered to detect and describe regions of relevant asymmetry. Three methods are provided to analyze the asymmetries: the global asymmetry score uses cluster features to quantify overall asymmetry, the local asymmetry score quantifies asymmetry in user-defined regions of interest, and the asymmetry similarity measure quantifies pairwise similarity of individual asymmetry. The scores and image distances generated by this tool are shown to correlate highly with asymmetry ratings assigned by an expert.

A landmark-free framework for the detection and description of shape differences in embryos.

This paper introduces a new method to quantify and characterize shape changes during early facial development without the use of landmarks. Landmarks are traditionally used in morphometric analysis, but very few can be identified reliably across all stages of embryonic development. This method uses deformable registration to produce a dense vector field describing the point correspondences between two images. Low and mid-level features are extracted from the deformable vector field to find regions of organized differences that are biologically relevant. These methods are shown to detect regions of difference when evaluated on chick embryo images warped with small magnitude deformations in regions critical to midfacial development.