{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T11:28:49Z","timestamp":1745926129502,"version":"3.40.4"},"publisher-location":"Cham","reference-count":5,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031832734"},{"type":"electronic","value":"9783031832741"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":61,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Auto-segmentation of gross tumor volumes (GTVs) in head and neck cancer (HNC) using MRI-guided radiotherapy (RT) images presents a significant challenge that can greatly enhance clinical workflows in radiation oncology. In this study, we developed a novel deep learning model based on the nnUNetv2 framework, augmented with an autoencoder architecture. Our model introduces the original training images as an additional input channel and incorporates an MSE loss function to improve segmentation accuracy. The model was trained on a dataset of 150 HNC patients, with a private evaluation of 50 test patients as part of the HNTS-MRG 2024 challenge. The aggregated Dice similarity coefficient (DSCagg) for metastatic lymph nodes (GTVn) reached 0.8516, while the primary tumor (GTVp) scored 0.7318, with an average DSCagg of 0.7917 across both structures. By introducing an autoencoder output channel and combining dice loss with mean squared error (MSE) loss, the enhanced nnUNet architecture effectively learned additional image features to enhance segmentation accuracy. These findings suggest that deep learning models like our modified nnUNetv2 framework can significantly improve auto-segmentation accuracy in MRI-guided RT for HNC, contributing to more precise and efficient clinical workflows.<\/jats:p>","DOI":"10.1007\/978-3-031-83274-1_17","type":"book-chapter","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T12:42:41Z","timestamp":1740919361000},"page":"222-229","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing nnUNetv2 Training with\u00a0Autoencoder Architecture for\u00a0Improved Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Yichen","family":"An","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhimin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiguo","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.radonc.2020.04.032","volume":"150","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg, A., Leger, S., Valli\u00e8res, M., L\u00f6ck, S.: Image biomarker standardisation initiative. Radiother. Oncol. 150, 20\u201322 (2020). https:\/\/doi.org\/10.1016\/j.radonc.2020.04.032","journal-title":"Radiother. Oncol."},{"issue":"10","key":"17_CR2","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., Jakab, A., Bauer, S., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2015). https:\/\/doi.org\/10.1109\/TMI.2014.2377694","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"17_CR3","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021). https:\/\/doi.org\/10.1038\/s41592-020-01008-z","journal-title":"Nat. Methods"},{"key":"17_CR4","doi-asserted-by":"publisher","unstructured":"Jiang, H., Haimerl, J., Gu, X., Lu, W.: A general web-based platform for automatic delineation of head and neck gross tumor volumes in PET\/CT images. In: Head and Neck Tumor Segmentation and Outcome Prediction: Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, 22 September 2022, Proceedings, vol. 13666, pp. 47\u201353. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-27420-6_4","DOI":"10.1007\/978-3-031-27420-6_4"},{"key":"17_CR5","unstructured":"Tang, M., Qu, R., Chen, J., Qian, J.: Auto-segmentation of head and neck tumors using MRI: HNTS-MRG 2024 challenge. arXiv (2024). https:\/\/arxiv.org\/abs\/2404.09556"}],"container-title":["Lecture Notes in Computer Science","Head and Neck Tumor Segmentation for MR-Guided Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-83274-1_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T10:44:38Z","timestamp":1745923478000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-83274-1_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031832734","9783031832741"],"references-count":5,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-83274-1_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"3 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HNTSMRG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hntsmrg2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/hntsmrg24.grand-challenge.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}