{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:20:40Z","timestamp":1774444840923,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T00:00:00Z","timestamp":1717718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Portuguese Foundation for Science and Technology","award":["PD\/BDE\/150624\/2020"],"award-info":[{"award-number":["PD\/BDE\/150624\/2020"]}]},{"DOI":"10.13039\/501100001871","name":"Bee2Fire SA","doi-asserted-by":"publisher","award":["PD\/BDE\/150624\/2020"],"award-info":[{"award-number":["PD\/BDE\/150624\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BioMedInformatics"],"abstract":"<jats:p>Current computer vision models require a significant amount of annotated data to improve their performance in a particular task. However, obtaining the required annotated data is challenging, especially in medicine. Hence, data augmentation techniques play a crucial role. In recent years, generative models have been used to create artificial medical images, which have shown promising results. This study aimed to use a state-of-the-art generative model, StyleGAN3, to generate realistic synthetic abdominal magnetic resonance images. These images will be evaluated using quantitative metrics and qualitative assessments by medical professionals. For this purpose, an abdominal MRI dataset acquired at Garcia da Horta Hospital in Almada, Portugal, was used. A subset containing only axial gadolinium-enhanced slices was used to train the model. The obtained Fr\u00e9chet inception distance value (12.89) aligned with the state of the art, and a medical expert confirmed the significant realism and quality of the images. However, specific issues were identified in the generated images, such as texture variations, visual artefacts and anatomical inconsistencies. Despite these, this work demonstrated that StyleGAN3 is a viable solution to synthesise realistic medical imaging data, particularly in abdominal imaging.<\/jats:p>","DOI":"10.3390\/biomedinformatics4020082","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T11:56:48Z","timestamp":1717761408000},"page":"1506-1518","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Abdominal MRI Unconditional Synthesis with Medical Assessment"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8002-0391","authenticated-orcid":false,"given":"Bernardo","family":"Gon\u00e7alves","sequence":"first","affiliation":[{"name":"Physics Department, NOVA School of Science and Technology, 2829-516 Caparica, Portugal"},{"name":"Bee2Fire SA, Edi. Inov. Point, Sala 2.16, TagusValley-Tecnopolo do Vale do Tejo, R. Jos\u00e9 Dias Sim\u00e3o, Alferrarede, 2200-062 Abrantes, Portugal"}]},{"given":"Mariana","family":"Silva","sequence":"additional","affiliation":[{"name":"Physics Department, NOVA School of Science and Technology, 2829-516 Caparica, Portugal"}]},{"given":"Lu\u00edsa","family":"Vieira","sequence":"additional","affiliation":[{"name":"Instituto de Biofisica e Engenharia Biom\u00e9dica, Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3823-1184","authenticated-orcid":false,"given":"Pedro","family":"Vieira","sequence":"additional","affiliation":[{"name":"Physics Department, NOVA School of Science and Technology, 2829-516 Caparica, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100723","DOI":"10.1016\/j.imu.2021.100723","article-title":"An overview of deep learning in medical imaging","volume":"26","year":"2021","journal-title":"Inform. 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