{"doi":"10.1242/dev.200621","title":"DeepProjection: specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning","abstract":"The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here, we present DeepProjection (DP), a trainable projection algorithm based on deep learning. This algorithm is trained on user-generated training data to locally classify 3D stack content, and to rapidly and robustly predict binary masks containing the target content, e.g. tissue boundaries, while masking highly fluorescent out-of-plane artifacts. A projection of the masked 3D stack then yields background-free 2D images with undistorted fluorescence intensity values. The binary masks can further be applied to other fluorescent channels or to extract local tissue curvature. DP is designed as a first processing step than can be followed, for example, by segmentation to track cell fate. We apply DP to follow the dynamic movements of 2D-tissue sheets during dorsal closure in Drosophila embryos and of the periderm layer in the elongating Danio embryo. DeepProjection is available as a fully documented Python package.","journal":"Development","year":2022,"id":252905,"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":23,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9553,"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":897607,"name":"X. Wang","orcid":"0000-0003-2660-0859","position":1,"is_corresponding":false},{"id":543206,"name":"Stephanie M. Fogerson","orcid":null,"position":2,"is_corresponding":false},{"id":897608,"name":"Nitya Ramkumar","orcid":"0000-0002-4086-4562","position":3,"is_corresponding":false},{"id":520009,"name":"Janice M. Crawford","orcid":null,"position":4,"is_corresponding":false},{"id":234837,"name":"Kenneth D. Poss","orcid":"0000-0002-6743-5709","position":5,"is_corresponding":false},{"id":291159,"name":"Stefano Di Talia","orcid":"0000-0001-9758-7925","position":6,"is_corresponding":false},{"id":520010,"name":"Daniel P. Kiehart","orcid":null,"position":7,"is_corresponding":false},{"id":307857,"name":"Christoph F. Schmidt","orcid":"0000-0003-2864-6973","position":8,"is_corresponding":false},{"id":897606,"name":"Daniel Haertter","orcid":"0000-0002-9582-6141","position":0,"is_corresponding":true}],"reference_count":28,"raw_metadata":null,"created_at":"2026-07-19T00:24:50.819822Z","pmid":"36178108","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":[]}