{"doi":"10.1101/2022.08.26.505447","title":"DEEP LEARNING ENABLED MULTI-ORGAN SEGMENTATION OF MOUSE EMBRYOS","abstract":"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 STATEMENT We 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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":301758,"datarank":0.23494682922683108,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.07015498592661464,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.07015498592661464,"corpus_percentile":38.4543977721049,"corpus_rank":7957,"citation_count":2,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.935,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":557936,"name":"A. Murat Maga","orcid":"0000-0002-7921-9018","position":1,"is_corresponding":false},{"id":557935,"name":"Sara Rolfe","orcid":"0000-0001-9514-7774","position":0,"is_corresponding":true}],"reference_count":16,"raw_metadata":null,"created_at":"2026-07-19T00:32:06.309890Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}