{"doi":"10.1002/mp.18002","title":"Robust real‐time segmentation of bio‐morphological features in human cherenkov imaging during radiotherapy via deep learning","abstract":"BACKGROUND: Cherenkov imaging enables real-time visualization of megavoltage X-ray or electron beam delivery to the patient during radiation therapy (RT). Bio-morphological features, such as vasculature, seen in these images are patient-specific signatures that can be used for verification of positioning and motion management that are essential to precise RT treatment. However, no concerted analysis of this biological feature-based tracking has been utilized until now because of the slow speed and accuracy of conventional image processing for feature segmentation. PURPOSE: This study aims to demonstrate the first deep learning framework for such an application, achieving video frame rate processing. MATERIALS AND METHODS: To address the challenge of limited annotation of bio-morphological features in Cherenkov images, a transfer learning strategy was applied. A fundus photography dataset including 20,529 patch retina images with ground-truth vessel annotation was used to pre-train a ResNet based segmentation framework. Subsequently, a small Cherenkov dataset (1483 images from 212 treatment fractions of 19 breast cancer patients) with known annotated vasculature masks was used to fine-tune the model for accurate segmentation prediction. RESULTS: The well-trained model was tested on clinical Cherenkov dataset which was not used in fine-tune steps. This deep learning framework achieved consistent and rapid segmentation of Cherenkov-imaged bio-morphological features on a test dataset containing 19 patients (179 images), including subcutaneous veins, scars, and pigmented skin. The average segmentation by the model achieved a Dice score of 0.85 and required less than 0.7 ms processing time per instance. CONCLUSIONS: The model demonstrated outstanding consistency against input image variances and speed compared to conventional manual segmentation methods, laying the foundation for online segmentation in real-time monitoring in a prospective setting.","journal":"Medical Physics","year":2025,"id":538362,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9565,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1322520,"name":"Yao Chen","orcid":"0000-0002-3534-7093","position":1,"is_corresponding":false},{"id":63213,"name":"Lesley A. Jarvis","orcid":"0000-0003-1930-6707","position":2,"is_corresponding":false},{"id":425889,"name":"Yucheng Tang","orcid":"0000-0002-6008-9700","position":3,"is_corresponding":false},{"id":250015,"name":"David J. Gladstone","orcid":"0000-0002-4086-0297","position":4,"is_corresponding":false},{"id":377168,"name":"Kimberley S. Samkoe","orcid":"0000-0002-8234-2308","position":5,"is_corresponding":false},{"id":250016,"name":"Brian W. Pogue","orcid":"0000-0002-9887-670X","position":6,"is_corresponding":false},{"id":250017,"name":"Petr Brůža","orcid":"0000-0002-6196-5872","position":7,"is_corresponding":false},{"id":250013,"name":"Rongxiao Zhang","orcid":"0000-0002-0423-7828","position":8,"is_corresponding":false},{"id":1424986,"name":"Shiru Wang","orcid":"0009-0009-1585-482X","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-19T02:52:21.389196Z","pmid":"39314506","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":[]}