{"doi":"10.1117/1.jmi.12.2.025003","title":"WS-SfMLearner: self-supervised monocular depth and ego-motion estimation on surgical videos with unknown camera parameters","abstract":"Purpose: Accurate depth estimation in surgical videos is a pivotal component of numerous image-guided surgery procedures. However, creating ground truth depth maps for surgical videos is often infeasible due to challenges such as inconsistent illumination and sensor noise. As a result, self-supervised depth and ego-motion estimation frameworks are gaining traction, eliminating the need for manually annotated depth maps. Despite the progress, current self-supervised methods still rely on known camera intrinsic parameters, which are frequently unavailable or unrecorded in surgical environments. We address this gap by introducing a self-supervised system capable of jointly predicting depth maps, camera poses, and intrinsic parameters, providing a comprehensive solution for depth estimation under such constraints. Approach: We developed a self-supervised depth and ego-motion estimation framework, incorporating a cost volume-based auxiliary supervision module. This module provides additional supervision for predicting camera intrinsic parameters, allowing for robust estimation even without predefined intrinsics. The system was rigorously evaluated on a public dataset to assess its effectiveness in simultaneously predicting depth, camera pose, and intrinsic parameters. Results: The experimental results demonstrated that the proposed method significantly improved the accuracy of ego-motion and depth prediction, even when compared with methods incorporating known camera intrinsics. In addition, by integrating our cost volume-based supervision, the accuracy of camera parameter estimation, including intrinsic parameters, was further enhanced. Conclusions: We present a self-supervised system for depth, ego-motion, and intrinsic parameter estimation, effectively overcoming the limitations imposed by unknown or missing camera intrinsics. The experimental results confirm that the proposed method outperforms the baseline techniques, offering a robust solution for depth estimation in complex surgical video scenarios, with broader implications for improving image-guided surgery systems.","journal":"Journal of Medical Imaging","year":2025,"id":528181,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.95,"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":413092,"name":"Jack H. Noble","orcid":"0000-0002-0973-505X","position":1,"is_corresponding":false},{"id":1166157,"name":"Ange Lou","orcid":"0000-0002-3983-8877","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":null,"created_at":"2026-07-19T02:50:48.492873Z","pmid":"40313401","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":[]}