{"doi":"10.1117/1.jmi.10.6.064502","title":"U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging","abstract":"PurposeGiven the dependence of radiomic-based computer-aided diagnosis artificial intelligence on accurate lesion segmentation, we assessed the performances of 2D and 3D U-Nets in breast lesion segmentation on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) relative to fuzzy c-means (FCM) and radiologist segmentations.ApproachUsing 994 unique breast lesions imaged with DCE-MRI, three segmentation algorithms (FCM clustering, 2D and 3D U-Net convolutional neural networks) were investigated. Center slice segmentations produced by FCM, 2D U-Net, and 3D U-Net were evaluated using radiologist segmentations as truth, and volumetric segmentations produced by 2D U-Net slices and 3D U-Net were compared using FCM as a surrogate reference standard. Fivefold cross-validation by lesion was conducted on the U-Nets; Dice similarity coefficient (DSC) and Hausdorff distance (HD) served as performance metrics. Segmentation performances were compared across different input image and lesion types.Results2D U-Net outperformed 3D U-Net for center slice (DSC, HD p < 0.001) and volume segmentations (DSC, HD p < 0.001). 2D U-Net outperformed FCM in center slice segmentation (DSC p < 0.001). The use of second postcontrast subtraction images showed greater performance than first postcontrast subtraction images using the 2D and 3D U-Net (DSC p < 0.05). Additionally, mass segmentation outperformed nonmass segmentation from first and second postcontrast subtraction images using 2D and 3D U-Nets (DSC, HD p < 0.001).ConclusionsResults suggest that 2D U-Net is promising in segmenting mass and nonmass enhancing breast lesions from first and second postcontrast subtraction MRIs and thus could be an effective alternative to FCM or 3D U-Net.","journal":"Journal of Medical Imaging","year":2023,"id":353310,"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":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9502,"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":1100038,"name":"Roma Bhattacharjee","orcid":null,"position":1,"is_corresponding":false},{"id":450235,"name":"Jordan Fuhrman","orcid":null,"position":2,"is_corresponding":false},{"id":727574,"name":"Karen Drukker","orcid":"0000-0001-6544-3476","position":3,"is_corresponding":false},{"id":250873,"name":"Qiyuan Hu","orcid":"0000-0002-3326-6441","position":4,"is_corresponding":false},{"id":783215,"name":"Alexandra Edwards","orcid":null,"position":5,"is_corresponding":false},{"id":295256,"name":"Deepa Sheth","orcid":"0000-0003-0314-3333","position":6,"is_corresponding":false},{"id":250875,"name":"Maryellen L. Giger","orcid":"0000-0001-5482-9728","position":7,"is_corresponding":false},{"id":931849,"name":"Lindsay Douglas","orcid":"0000-0002-4695-6441","position":0,"is_corresponding":true}],"reference_count":18,"raw_metadata":null,"created_at":"2026-07-19T01:12:58.542950Z","pmid":"37990686","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":[]}