{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T16:32:04Z","timestamp":1787157124413,"version":"build-2736575974"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["13GW0473A"],"award-info":[{"award-number":["13GW0473A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["547369510"],"award-info":[{"award-number":["547369510"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Purpose:<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>Ablation therapies are a treatment option for cancer patients, particularly for conditions such as spinal metastases and liver tumors. Precisely delineating ablation zones is essential for accurately assessing treatment success. However, research in MRI-guided interventions remains limited for automated segmentation approaches and quantitative analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Methods:<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>We developed a framework for automated segmentation of ablation zones following thermal interventions. The performance was tested on two representative types of clinical cases from different clinical sites: post-ablative liver lesions and spinal metastases. Four leading neural networks (nnUNet, TransUNet, SwinUNETR, and SwinUNETR-V2) were evaluated for their segmentation accuracy in segmenting necrotic tissue. Additionally, a statistical analysis was performed to investigate the influence of an optimized image ROI selection on the segmentation performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Results:<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>The nnUNet achieved the highest segmentation performance, with a Dice Similarity Coefficient of 83.3 \u00b1 13.2% for spinal metastases and 82.2 \u00b1 12.4% for liver lesions. The statistical analysis revealed that cropping images to a standardized ROI size provided the optimal balance between user interaction and networks\u2019 performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Conclusion:<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>The automated segmentation accuracies are comparable to the inter-rater variability observed among radiologists for spinal imaging and establish the new state-of-the-art for liver MRIs, indicating the potential to facilitate the clinical routine for interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11548-026-03775-0","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T16:10:58Z","timestamp":1787155858000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic MRI-Based segmentation of ablation zones in spine and liver: A comparative study of network performance and influencing factors"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7050-8828","authenticated-orcid":false,"given":"Janine","family":"Rothert","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sylvia","family":"Saalfeld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcel","family":"Gutberlet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Wacker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"D\u00fcx","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bennet","family":"Hensen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathias","family":"Becker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georg","family":"Rose","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georg","family":"Hille","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,19]]},"reference":[{"issue":"3","key":"3775_CR1","first-page":"229","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A (2024) Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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For this type of study formal consent is not required.","order":2,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}}]}}