Abstract

PURPOSE/OBJECTIVE(S): A classical approach for evaluating normal tissue radiation response is to count the number of intestinal regenerating crypts in mouse histological images acquired after abdominal radiation. However, manual counting is time-consuming and subject to inter-observer variations. The goal of this study is to build a deep learning-based pipeline for automatically identifying intestinal regenerating crypts to facilitate high-throughput studies.MATERIALS/METHODS: Sixty-six healthy C57BL/6 female mice underwent 16 MeV whole abdominal electron irradiation. The small bowel was collected from each mouse 4 days post-irradiation, and 9 jejunal cross-sections from each were processed together in a single slide. The slides were stained with hematoxylin and eosin (H&E) and subsequently scanned (x20), providing one electronic histological image per mouse. Regenerating crypts, consisting of more than 10 basophilic crypt epithelial cells, were manually identified using point annotations in histological images. The pipeline was built to take the input of the image containing 9 cross sections and automatically identify the regenerating crypts on each cross section. It mainly consists of two components, cross section segmentation using intensity thresholding and morphological operations and crypt identification using a UNet. The dataset was randomly split into 46, 10, and 10 slide images for UNet training, validation, and testing. Each slide image was split into grid tiles with a voxel size of 200?*?200, and 40?*?40 square masks were placed with centers at manual point annotations on tiles with regenerating crypts. 5203/5198 tiles (w/wo crypt mask) were extracted to train UNet by minimizing dice loss. The mask probability map generated by the UNet was post-processed to identify the crypt position. Postprocessing hyperparameters were tuned using the validation dataset. The model accuracy was evaluated using the testing dataset by computing the mean absolute error (MAE) of the crypt number averaged across all cross sections.RESULTS: The number of regenerating crypts on testing cross sections ranges from 1 to 63. The testing cross-section-wise MAE achieved by the platform is 3.5±4.8 crypts. 81.25% of testing cross sections have absolute number differences less than or equal to 5 crypts.CONCLUSION: Our established deep learning-based pipeline can accurately count the number of regenerating crypts in mouse intestinal histological images. We have integrated it into an online platform that enables automatic crypt identification and allows users to interactively modify auto-identified crypt annotations. The acquired annotations from the platform will be used to finetune the deep learning model to achieve better identification performance.

View details for DOI 10.1016/j.ijrobp.2023.06.451

View details for PubMedID 37784305