{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T19:44:03Z","timestamp":1793130243581,"version":"4.5.0"},"reference-count":46,"publisher":"IOP Publishing","issue":"19","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2025,10,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Twin-to-twin transfusion syndrome (TTTS) is a complex prenatal condition in which monochorionic twins experience an imbalance in blood flow due to abnormal vascular connections in the shared placenta. Fetoscopic laser photocoagulation is the first-line treatment for TTTS, aimed at coagulating these abnormal connections. However, the procedure is complicated by a limited field of view, occlusions, poor-quality endoscopic images, and distortions caused by artifacts. To optimize the visualization of placental vessels during surgical procedures, we propose Hybrid-MedNet, a novel hybrid CNN-transformer network that incorporates multi-dimensional deep feature learning techniques. The network introduces a BiPath tokenization module that enhances vessel boundary detection by capturing both channel dependencies and spatial features through parallel attention mechanisms. A context-aware transformer block addresses the weak inductive bias problem in traditional transformers while preserving spatial relationships crucial for accurate vessel identification in distorted fetoscopic images. Furthermore, we develop a multi-scale trifusion module that integrates multi-dimensional features to capture rich vascular representations from the encoder and facilitate precise vessel information transfer to the decoder for improved segmentation accuracy. Experimental results show that our approach achieves a Dice score of 95.40% on fetoscopic images, outperforming ten state-of-the-art segmentation methods. The consistent superior performance across four segmentation tasks and ten distinct datasets confirms the robustness and effectiveness of our method for diverse and complex medical imaging applications.<\/jats:p>","DOI":"10.1088\/1361-6560\/ae0976","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T22:51:54Z","timestamp":1758322314000},"page":"195016","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Hybrid-MedNet: a hybrid CNN-transformer network with multi-dimensional feature fusion for medical image segmentation"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2022-5827","authenticated-orcid":true,"given":"Yumna","family":"Memon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"pmbae0976bib1","type":"conference-proceedings","article-title":"Automated vasculature extraction from placenta images","volume":"vol 7962","author":"Almoussa","year":"2011"},{"key":"pmbae0976bib2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.6.1.014006","type":"journal-article","article-title":"Recurrent residual u-net for medical image segmentation","volume":"6","author":"Alom","year":"2019","journal-title":"J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-05-31","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-09-19","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-09-30","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}