{"doi":"10.3389/fimmu.2022.981825","title":"A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data","abstract":"Highly multiplexed, single-cell imaging has revolutionized our understanding of spatial cellular interactions associated with health and disease. With ever-increasing numbers of antigens, region sizes, and sample sizes, multiplexed fluorescence imaging experiments routinely produce terabytes of data. Fast and accurate processing of these large-scale, high-dimensional imaging data is essential to ensure reliable segmentation and identification of cell types and for characterization of cellular neighborhoods and inference of mechanistic insights. Here, we describe RAPID, a Real-time, GPU-Accelerated Parallelized Image processing software for large-scale multiplexed fluorescence microscopy Data. RAPID deconvolves large-scale, high-dimensional fluorescence imaging data, stitches and registers images with axial and lateral drift correction, and minimizes tissue autofluorescence such as that introduced by erythrocytes. Incorporation of an open source CUDA-driven, GPU-assisted deconvolution produced results similar to fee-based commercial software. RAPID reduces data processing time and artifacts and improves image contrast and signal-to-noise compared to our previous image processing pipeline, thus providing a useful tool for accurate and robust analysis of large-scale, multiplexed, fluorescence imaging data.","journal":"Frontiers in Immunology","year":2022,"id":260220,"datarank":0.42498200160843247,"base_score":2.833213344056216,"endowment":2.833213344056216,"self_citation_contribution":0.42498200160843247,"citation_network_contribution":0.0,"self_endowment_contribution":0.42498200160843247,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":16,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9428,"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":913963,"name":"Marc A. Baertsch","orcid":null,"position":1,"is_corresponding":false},{"id":615289,"name":"John W. Hickey","orcid":"0000-0001-9961-7673","position":2,"is_corresponding":false},{"id":55258,"name":"Yury Goltsev","orcid":"0000-0003-4079-7969","position":3,"is_corresponding":false},{"id":226345,"name":"Andrew J. Rech","orcid":"0000-0002-6585-8839","position":4,"is_corresponding":false},{"id":913385,"name":"Lucas Mani","orcid":"0009-0002-8638-4705","position":5,"is_corresponding":false},{"id":74081,"name":"Erna Forgó","orcid":"0000-0001-6320-0905","position":6,"is_corresponding":false},{"id":275400,"name":"Christina S. Kong","orcid":"0000-0003-2098-6842","position":7,"is_corresponding":false},{"id":55231,"name":"Sizun Jiang","orcid":"0000-0001-6149-3142","position":8,"is_corresponding":false},{"id":35731,"name":"Garry P. Nolan","orcid":"0000-0002-8862-9043","position":9,"is_corresponding":false},{"id":270918,"name":"Eben L. Rosenthal","orcid":"0000-0003-0294-5203","position":10,"is_corresponding":false},{"id":270912,"name":"Guolan Lu","orcid":"0000-0002-5023-3465","position":0,"is_corresponding":true}],"reference_count":25,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:26:03.189798Z","pmid":"36211386","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":[]}