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Although U-Net variants with popular encoder backbones are widely used, the coupled effects of encoder selection, training duration, and computational cost, as well as comparisons against volumetric architectures such as V-Net, remain insufficiently standardized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0021_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose a unified benchmarking framework that evaluates a family of multi-encoder 2D U-Net models together with an optional 3D V-Net baseline under the same preprocessing, input construction, and 3-fold cross-validation protocol on LiTS17. Multiple backbones (VGG16\/VGG19\/ResNet34\/ResNet50\/ResNet101\/MobileNetV2) are assessed under 15\/50\/100-epoch schedules, and performance is reported using overlap and detection metrics (Dice, IoU, precision, recall) alongside efficiency indicators (training time and model complexity when available) to characterize the accuracy\u2013efficiency trade-off.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0021_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Results show liver segmentation rapidly reaches near-ceiling performance across models, while tumor segmentation benefits markedly from longer training and stronger encoders, especially for small or low-contrast lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0021_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Overall, the study provides a reproducible protocol and practical guidance for selecting segmentation models that balance accuracy, robustness, and deployment\u00a0cost.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/bmt-2026-0021","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:56:21Z","timestamp":1771264581000},"page":"269-284","source":"Crossref","is-referenced-by-count":2,"title":["Multi-encoder U-Net benchmarking for LiTS17 Liver\u2013Tumor segmentation: accuracy\u2013efficiency trade-offs across training durations"],"prefix":"10.1515","volume":"71","author":[{"given":"Yuguang","family":"Ye","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Quanzhou Normal University , Quanzhou , China"},{"name":"Fujian Provincial Key Laboratory of Data Intensive Computing , Quanzhou , China"},{"name":"Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University , Quanzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kavimbi","family":"Chipusu","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Quanzhou Normal University , Quanzhou , China"},{"name":"Fujian Provincial Key Laboratory of Data Intensive Computing , Quanzhou , China"},{"name":"Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University , Quanzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taisheng","family":"Zeng","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Quanzhou Normal University , Quanzhou , China"},{"name":"Fujian Provincial Key Laboratory of Data Intensive Computing , Quanzhou , China"},{"name":"Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University , Quanzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusi","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Quanzhou Normal University , Quanzhou , China"},{"name":"Fujian Provincial Key Laboratory of Data Intensive Computing , Quanzhou , China"},{"name":"Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University , Quanzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bijiao","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology , Huaqiao University Affiliated Strait Hospital , Quanzhou , Fujian , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology , Huaqiao University Affiliated Strait Hospital , Quanzhou , Fujian , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwei","family":"Guo","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Quanzhou Normal University , Quanzhou , China"},{"name":"Fujian Provincial Key Laboratory of Data Intensive Computing , Quanzhou , China"},{"name":"Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University , Quanzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianlong","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Quanzhou Normal University , Quanzhou , China"},{"name":"Fujian Provincial Key Laboratory of Data Intensive Computing , Quanzhou , China"},{"name":"Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University , Quanzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"2026100814512983788_j_bmt-2026-0021_ref_001","doi-asserted-by":"crossref","unstructured":"Alshammari, K, Ginawi, I, Sherfi, H, Ahmed, H. 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