https://m2.mtmt.hu/ hun 2026-04-17 01:07 JournalArticle 33436891 VALIDATED true 0 false 2026-03-19T10:45:01.224+0000 2026-03-19T10:38:59.277+0000 2022-12-22T22:55:16.246+0000 true 521 Szuper Admin /api/admin/521 Admin true 2024-11-16T15:26:14.220+0000 2022-12-22T22:55:16.516+0000 true 521 Szuper Admin /api/admin/521 Admin true false false true 24 24 /api/publicationtype/24 PublicationType 1 true 24 JournalArticle true 10000059 Article true 24 24 /api/publicationtype/24 PublicationType 1 true 24 JournalArticle /api/subtype/10000059 Szakcikk SubType 101 true 10000059 true 1 /api/category/1 Category true 1 Segota, Sandi Baressi Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach true true /api/journal/10035136 REVIEWED true false 10035136 false 10035136 true 2079-7737 Journal FOREIGN 10 11 1134 1134 25 2021 Simple Summary: Bladder cancer is a common cancer of the urinary tract, characterized by high metastatic potential and recurrence. The research applies a transfer learning approach on CT images (frontal, axial, and saggital axes) for the purpose of semantic segmentation of areas affected by bladder cancer. A system consisting of AlexNet network for plane recognition, using transfer learning-based U-net networks for the segmentation task. Achieved results show that the proposed system has a high performance, suggesting possible use in clinical practice.Abstract: Urinary bladder cancer is one of the most common cancers of the urinary tract. This cancer is characterized by its high metastatic potential and recurrence rate. Due to the high metastatic potential and recurrence rate, correct and timely diagnosis is crucial for successful treatment and care. With the aim of increasing diagnosis accuracy, artificial intelligence algorithms are introduced to clinical decision making and diagnostics. One of the standard procedures for bladder cancer diagnosis is computer tomography (CT) scanning. In this research, a transfer learning approach to the semantic segmentation of urinary bladder cancer masses from CT images is presented. The initial data set is divided into three sub-sets according to image planes: frontal (4413 images), axial (4993 images), and sagittal (996 images). First, AlexNet is utilized for the design of a plane recognition system, and it achieved high classification and generalization performances with an (AUC(micro)) over bar of 0.9999 and sigma(AUC(micro)) of 0.0006. Furthermore, by applying the transfer learning approach, significant improvements in both semantic segmentation and generalization performances were achieved. For the case of the frontal plane, the highest performances were achieved if pre-trained ResNet101 architecture was used as a backbone for U-net with (DSC) over bar up to 0.9587 and sigma(DSC) of 0.0059. When U-net was used for the semantic segmentation of urinary bladder cancer masses from images in the axial plane, the best results were achieved if pre-trained ResNet50 was used as a backbone, with a DSC up to 0.9372 and sigma(DSC) of 0.0147. Finally, in the case of images in the sagittal plane, the highest results were achieved with VGG-16 as a backbone. In this case, (DSC) over bar values up to 0.9660 with a sigma(DSC) of 0.0486 were achieved. From the listed results, the proposed semantic segmentation system worked with high performance both from the semantic segmentation and generalization standpoints. The presented results indicate that there is the possibility for the utilization of the semantic segmentation system in clinical practice. 2022 true true true false false GOLD 2026-03-19 false http://www.mdpi.com/journal/biology 0 0 0 0 0 0 0 0 0 0 0 0 2 2 Q1 false false false false 2026-06-17T09:38:59.208+0000 4 24 0 10 0 true Language 10002 /api/language/10002 Angol English true 2 true PersonAuthorship 105566449 /api/authorship/105566449 1 0.1 true false false Segota Sandi Baressi true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105566450 /api/authorship/105566450 2 0.1 false false false Lorencin Ivan true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105566451 /api/authorship/105566451 3 0.1 false false false Smolic Klara true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105566452 /api/authorship/105566452 4 0.1 false false false Andelic Nikola true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105566453 /api/authorship/105566453 5 0.1 false false false Markic Dean true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105566454 /api/authorship/105566454 6 0.1 false false true Mrzljak Vedran true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105566455 /api/authorship/105566455 7 0.1 false false false Stifanic Daniel true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105566456 /api/authorship/105566456 8 0.1 false false false Musulin Jelena true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105566457 /api/authorship/105566457 9 0.1 false false false Spanjol Josip true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105566458 /api/authorship/105566458 10 0.1 false true false Car Zlatan true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PublicationIdentifier 22582106 /api/publicationidentifier/22582106 PlainSource 6 /api/publicationsource/6 PublicationSourceType 10001 /api/publicationsourcetype/10001 true true true DOI DOI https://doi.org/@@@ true true 6 true IDENTICAL 10.3390/biology10111134 https://doi.org/10.3390/biology10111134 false true PublicationIdentifier 22582105 /api/publicationidentifier/22582105 PlainSource 1 /api/publicationsource/1 PublicationSourceType 10003 /api/publicationsourcetype/10003 false true true WoS WoS https://www.webofscience.com/wos/woscc/full-record/@@@ true true 1 true IDENTICAL 000726575400001 https://www.webofscience.com/wos/woscc/full-record/000726575400001 false true PublicationIdentifier 27933714 /api/publicationidentifier/27933714 PlainSource 3 /api/publicationsource/3 PublicationSourceType 10003 /api/publicationsourcetype/10003 false true true Scopus Scopus http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-@@@ true true 3 true IDENTICAL 85119016618 http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85119016618 false true Classification 10545 /api/classification/10545 true true Keyword 3693 /api/keyword/3693 DUPLUM true 3693 true Keyword 11965 /api/keyword/11965 true 11965 true Keyword 12390 /api/keyword/12390 true 12390 true Keyword 1585924 /api/keyword/1585924 true true Keyword 1645653 /api/keyword/1645653 true true SjrRating 11185206 /api/sjrrating/11185206 43 0.24 journal RatingType 10002 /api/ratingtype/10002 sjr true true ClassificationExternal 1101 /api/classificationexternal/1101 true 1101 true Q1 DIRECT true true /api/publication/33436891 <div class="JournalArticle Publication long-list"> <div class="authors"> <img title="Idézőközlemény" style="float: left" src="/frontend/resources/grid/publication-citation-icon.png"> <div class="autype autype0"> <span class="author-name" >Segota Sandi Baressi </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Lorencin Ivan </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Smolic Klara </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Andelic Nikola </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Markic Dean </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Mrzljak Vedran ✉ </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Stifanic Daniel </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Musulin Jelena </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Spanjol Josip </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Car Zlatan </span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;33436891" target="_blank">Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach</a></div> <div> <span class="journal-title">BIOLOGY-BASEL</span> <span class="journal-issn">( <a target="_blank" href="https://portal.issn.org/resource/ISSN/2079-7737">2079-7737</a>)</span>: <span class="journal-volume">10</span> <span class="journal-issue">11</span> <span class="page"> Paper 1134. 25 p. </span> <span class="year">(2021)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="10.3390/biology10111134" target="_blank" href="https://doi.org/10.3390/biology10111134"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="000726575400001" target="_blank" href="https://www.webofscience.com/wos/woscc/full-record/000726575400001"> WoS </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="85119016618" target="_blank" href="http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85119016618"> Scopus </a> </span> </span> <div class="publication-citation"> <a target="_blank" href="/api/publication?cond=citations.related;eq;33436891&sort=publishedYear,desc&sort=title"> Idézett közlemények száma: 2 </a> </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 33436891</span> | <span class="status-data status-VALIDATED"> Egyeztetett </span> Idéző | <span class="type-subtype">Folyóiratcikk ( Szakcikk ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">WoS-XML</span> </div> <div class="lastModified">Utolsó módosítás: 2026.03.19. 11:38 Csernyik Rita (MTMT központi admin) </div> </div></div>