{"doi":"10.1364/boe.481657","title":"Segmentation of beating embryonic heart structures from 4-D OCT images using deep learning","abstract":"Optical coherence tomography (OCT) has been used to investigate heart development because of its capability to image both structure and function of beating embryonic hearts. Cardiac structure segmentation is a prerequisite for the quantification of embryonic heart motion and function using OCT. Since manual segmentation is time-consuming and labor-intensive, an automatic method is needed to facilitate high-throughput studies. The purpose of this study is to develop an image-processing pipeline to facilitate the segmentation of beating embryonic heart structures from a 4-D OCT dataset. Sequential OCT images were obtained at multiple planes of a beating quail embryonic heart and reassembled to a 4-D dataset using image-based retrospective gating. Multiple image volumes at different time points were selected as key-volumes, and their cardiac structures including myocardium, cardiac jelly, and lumen, were manually labeled. Registration-based data augmentation was used to synthesize additional labeled image volumes by learning transformations between key-volumes and other unlabeled volumes. The synthesized labeled images were then used to train a fully convolutional network (U-Net) for heart structure segmentation. The proposed deep learning-based pipeline achieved high segmentation accuracy with only two labeled image volumes and reduced the time cost of segmenting one 4-D OCT dataset from a week to two hours. Using this method, one could carry out cohort studies that quantify complex cardiac motion and function in developing hearts.","journal":"Biomedical Optics Express","year":2023,"id":403415,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9487,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":393646,"name":"Brecken J. Blackburn","orcid":null,"position":1,"is_corresponding":false},{"id":391990,"name":"Michael W. Jenkins","orcid":"0000-0002-8908-5383","position":2,"is_corresponding":false},{"id":523665,"name":"Michiko Watanabe","orcid":"0000-0002-6295-6759","position":3,"is_corresponding":false},{"id":794902,"name":"Stephanie Ford","orcid":"0000-0002-0493-640X","position":4,"is_corresponding":false},{"id":523662,"name":"Maryse Lapierre-Landry","orcid":"0000-0002-9583-6876","position":5,"is_corresponding":false},{"id":391989,"name":"Andrew M. Rollins","orcid":"0000-0002-7780-4862","position":6,"is_corresponding":false},{"id":753321,"name":"Shan Ling","orcid":"0000-0002-4160-3751","position":0,"is_corresponding":true}],"reference_count":56,"raw_metadata":null,"created_at":"2026-07-19T01:20:36.280647Z","pmid":"37206115","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":[]}