{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T15:09:51Z","timestamp":1792681791532,"version":"4.5.0"},"reference-count":37,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Medicine and Pharmacy of Craiova"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Background: The ultrasound is one of the most used medical imaging investigations worldwide. It is non-invasive and effective in assessing liver tumors or other types of parenchymal changes. Methods: The aim of the study was to build a deep learning model for image segmentation in ultrasound video investigations. The dataset used in the study was provided by the University of Medicine and Pharmacy Craiova, Romania and contained 50 video examinations from 49 patients. The mean age of the patients in the cohort was 69.57. Regarding presence of a subjacent liver disease, 36.73% had liver cirrhosis and 16.32% had chronic viral hepatitis (5 patients: chronic hepatitis C and 3 patients: chronic hepatitis B). Frames were extracted and cropped from each examination and an expert gastroenterologist labelled the lesions in each frame. After labelling, the labels were exported as binary images. A deep learning segmentation model (U-Net) was trained with focal Tversky loss as a loss function. Two models were obtained with two different sets of parameters for the loss function. The performance metrics observed were intersection over union and recall and precision. Results: Analyzing the intersection over union metric, the first segmentation model obtained performed better compared to the second model: 0.8392 (model 1) vs. 0.7990 (model 2). The inference time for both models was between 32.15 milliseconds and 77.59 milliseconds. Conclusions: Two segmentation models were obtained in the study. The models performed similarly during training and validation. However, one model was trained to focus on hard-to-predict labels. The proposed segmentation models can represent a first step in automatically extracting time-intensity curves from CEUS examinations.<\/jats:p>","DOI":"10.3390\/life12111877","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:28:58Z","timestamp":1668479338000},"page":"1877","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1524-2494","authenticated-orcid":false,"given":"M\u0103d\u0103lin","family":"M\u0103muleanu","sequence":"first","affiliation":[{"name":"Department of Automatic Control and Electronics, University of Craiova, 200585 Craiova, Romania"},{"name":"Oncometrics S.R.L., 200677 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5353-4327","authenticated-orcid":false,"given":"Cristiana Marinela","family":"Urhu\u021b","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Emergency County Hospital of Craiova, 200642 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Larisa Daniela","family":"S\u0103ndulescu","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Constantin","family":"Kamal","sequence":"additional","affiliation":[{"name":"Oncometrics S.R.L., 200677 Craiova, Romania"},{"name":"Department of Pulmonology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana-Maria","family":"P\u0103tra\u0219cu","sequence":"additional","affiliation":[{"name":"Oncometrics S.R.L., 200677 Craiova, Romania"},{"name":"Department of Hematology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alin Gabriel","family":"Ionescu","sequence":"additional","affiliation":[{"name":"Oncometrics S.R.L., 200677 Craiova, Romania"},{"name":"Department of History of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mircea-Sebastian","family":"\u0218erb\u0103nescu","sequence":"additional","affiliation":[{"name":"Oncometrics S.R.L., 200677 Craiova, Romania"},{"name":"Department of Medical Informatics and Statistics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Costin Teodor","family":"Streba","sequence":"additional","affiliation":[{"name":"Oncometrics S.R.L., 200677 Craiova, Romania"},{"name":"Department of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"},{"name":"Department of Pulmonology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e21652","DOI":"10.1097\/MD.0000000000021652","article-title":"Diagnosis of Liver Tumors by Multimodal Ultrasound Imaging","volume":"99","author":"Hu","year":"2020","journal-title":"Medicine"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.annemergmed.2020.05.024","article-title":"Trends in Diagnostic Point-of-Care Ultrasonography Reimbursement for Medicare Beneficiaries Among the US Emergency Medicine Workforce, 2012 to 2016","volume":"76","author":"Birch","year":"2020","journal-title":"Ann. 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