{"doi":"10.1117/1.jmi.8.1.014004","title":"Validation and estimation of spleen volume via computer-assisted segmentation on clinically acquired CT scans","abstract":"Purpose: Deep learning is a promising technique for spleen segmentation. Our study aims to validate the reproducibility of deep learning-based spleen volume estimation by performing spleen segmentation on clinically acquired computed tomography (CT) scans from patients with myeloproliferative neoplasms. Approach: As approved by the institutional review board, we obtained 138 de-identified abdominal CT scans. A sum of voxel volume on an expert annotator’s segmentations establishes the ground truth (estimation 1). We used our deep convolutional neural network (estimation 2) alongside traditional linear estimations (estimation 3 and 4) to estimate spleen volumes independently. Dice coefficient, Hausdorff distance, R2 coefficient, Pearson R coefficient, the absolute difference in volume, and the relative difference in volume were calculated for 2 to 4 against the ground truth to compare and assess methods’ performances. We re-labeled on scan–rescan on a subset of 40 studies to evaluate method reproducibility. Results: Calculated against the ground truth, the R2 coefficients for our method (estimation 2) and linear method (estimation 3 and 4) are 0.998, 0.954, and 0.973, respectively. The Pearson R coefficients for the estimations against the ground truth are 0.999, 0.963, and 0.978, respectively (paired t-tests produced p < 0.05 between 2 and 3, and 2 and 4). Conclusion: The deep convolutional neural network algorithm shows excellent potential in rendering more precise spleen volume estimations. Our computer-aided segmentation exhibits reasonable improvements in splenic volume estimation accuracy.","journal":"Journal of Medical Imaging","year":2021,"id":193699,"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":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.5305,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":425889,"name":"Yucheng Tang","orcid":"0000-0002-6008-9700","position":1,"is_corresponding":false},{"id":425887,"name":"Riqiang Gao","orcid":"0000-0002-8729-1941","position":2,"is_corresponding":false},{"id":425888,"name":"Shunxing Bao","orcid":"0000-0001-6376-4292","position":3,"is_corresponding":false},{"id":291228,"name":"Yuankai Huo","orcid":"0000-0002-2096-8065","position":4,"is_corresponding":false},{"id":762255,"name":"Matthew T. McKenna","orcid":"0000-0001-5003-539X","position":5,"is_corresponding":false},{"id":225918,"name":"Michael R. Savona","orcid":"0000-0003-3763-5504","position":6,"is_corresponding":false},{"id":536218,"name":"Richard G. Abramson","orcid":"0000-0002-1200-0281","position":7,"is_corresponding":false},{"id":104551,"name":"Bennett A. Landman","orcid":"0000-0001-5733-2127","position":8,"is_corresponding":false},{"id":762254,"name":"Yiyuan Yang","orcid":"0000-0002-5320-095X","position":0,"is_corresponding":true}],"reference_count":23,"raw_metadata":null,"created_at":"2026-07-18T23:49:51.300073Z","pmid":"33634205","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":[]}