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However, this task remains challenging due to large anatomical variability, small organ sizes, inter\u2010slice discontinuities, and the computational demands of volumetric segmentation. We propose PVT3D\u2010ThoraxNet, a hybrid 2D\u20133D framework that integrates a Pyramid Vision Transformer (PVTv2) encoder with a convolutional 3D decoder via a novel 3D Context Encoder, enabling effective fusion of multi\u2010slice features. To further enhance structural consistency, we introduce a 3D Trainable Guided Filter (TGF) in the decoder for boundary refinement. On the Lung CT Segmentation Challenge (LCTSC) dataset across five thoracic organs (esophagus, heart, left lung, right lung, spinal cord), PVT3D\u2010ThoraxNet achieves a mean Dice Similarity Coefficient of 0.903 and a mean HD95 of 3.59\u2009mm. On a private thoracic CT dataset, it generalizes well with a mean Dice of 0.875 and a mean HD95 of 4.81\u2009mm, without dataset\u2010specific fine\u2010tuning. Compared with recent multi\u2010stage and transformer\u2010based approaches, our framework provides a lightweight, robust, and accurate solution for thoracic multi\u2010organ segmentation.<\/jats:p>","DOI":"10.1002\/ima.70318","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T12:24:02Z","timestamp":1771503842000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adapting\n                    <scp>2D<\/scp>\n                    Vision Transformer Backbones for\n                    <scp>3D<\/scp>\n                    Thoracic Multi\u2010Organ Segmentation"],"prefix":"10.1002","volume":"36","author":[{"given":"Levent","family":"Karacan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering Gaziantep University  Gaziantep T\u00fcrkiye"}]},{"given":"Hamdi Yal\u0131n","family":"Yal\u0131\u00e7","sequence":"additional","affiliation":[{"name":"Caria Health  Ankara T\u00fcrkiye"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2493-4022","authenticated-orcid":false,"given":"Alaettin","family":"U\u00e7an","sequence":"additional","affiliation":[{"name":"TIGA Information Technologies  Ankara T\u00fcrkiye"}]},{"given":"Ali Ya\u015far","family":"Yi\u011fit","sequence":"additional","affiliation":[{"name":"TIGA Information Technologies  Ankara T\u00fcrkiye"}]},{"given":"Adem Ali","family":"Y\u0131lmaz","sequence":"additional","affiliation":[{"name":"TIGA Information Technologies  Ankara T\u00fcrkiye"}]}],"member":"311","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"crossref","unstructured":"J.Long E.Shelhamer andT.Darrell \u201cFully Convolutional Networks for Semantic Segmentation. 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