Diffusible iodine-based contrast-enhanced micro-CT (diceCT) is a technique used to image soft tissue specimens using 3D x-ray microscopy. Staining soft tissues with iodine (I2KI) solution prior to scanning improves contrast for detailed visualization of internal organs, but iodine staining is also associated with tissue shrinkage which can interfere with quantitative analysis. It has been shown that stabilizing soft tissue with hydrogel can reduce shrinkage. We adopted these protocols for our lab, but, despite using hydrogel stabilization, we observed wrinkles in the external surfaces of E15.5 mouse embryos, qualitative evidence of considerable shrinkage. To quantitatively test for shrinkage, we compared the crown rump lengths (CRL) of mouse embryos measured from photos taken prior to the scanning process and then from diceCT scans. CRLs ranged from 12.4 to 20.0 mm in photos and 11.1 to 16.8 mm in scans. An average reduction of 12% resulted from the specimen preparation process and confirmed tissue shrinkage. Furthermore, the amount of shrinkage was not uniform across the specimens, complicating quantitative analysis based on diceCT. Our first hypothesis was that the iodine solution used to prepare the specimens was too acidic. We measured the pH of this solution and found a range from 4.5 - 6.4. To examine if a neutral pH reduced tissue shrinkage, we prepared specimens with a buffered iodine solution (pH 7.2). DiceCT scans of embryos in buffered iodine solution did not show reduced shrinkage compared to controls in the original solution. Further investigations will focus on other potential sources of shrinkage including the pH of other solutions and the time specimens spend in each step of the protocol. Continuing to investigate sources of tissue shrinkage in diceCT can lead to additional methods for shrinkage reduction, supporting more accurate quantitative analysis of diceCT.

Segmentation is an imaging technique commonly used to isolate an object of interest, such as an organ, from the background, or other objects in the image. When analyzing the shape of an anatomical structure, segmentation of that structure is often the first step in analysis. Precise anatomical segmentations are often created manually by subject experts, which is time-consuming, does not scale well, and can be prone to error since it is subjective. In this project, we aim to develop a machine-learning model to expedite whole-body surface segmentation from fetal mouse scans as part of an automated pipeline to detect asymmetry and abnormality in the facial region. 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. To generate segmentations required for training and validation of our deep learning model, the full body surface was manually segmented in 91 baseline scans from the IMPC’s Knockout Mouse Phenotyping Program (KOMP2) dataset. I trained a UNet with transformers (UNETR), on these segmentations that is able to estimate surface segmentations from new micro-CT mice images with an accuracy of 0.9. I am currently developing a fetal mouse full-body segmentation application powered by our deep learning model, SurfaceExtract, that will be made publicly available as an extension to the open-source image analysis platform, 3D Slicer. SurfaceExtract will be used by our lab to quickly and accurately generate segmentations of fetal mice as part of our lab’s automated facial asymmetry phenotyping pipeline.