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In this study, we developed a deep learning pipeline based on the SegResNet architecture, integrated into the Auto3DSeg framework, to achieve fully-automated segmentation on pre-treatment (pre-RT) and mid-treatment (mid-RT) MRI scans as part of the DLaBella29 team submission to the HNTS-MRG 2024 challenge. For Task 1, we used an ensemble of six SegResNet models with predictions fused via weighted majority voting. The models were pre-trained on both pre-RT and mid-RT image-mask pairs, then fine-tuned on pre-RT data, without any pre-processing. For Task 2, an ensemble of five SegResNet models was employed, with predictions fused using majority voting. Pre-processing for Task 2 involved setting all voxels more than 1\u00a0cm from the registered pre-RT masks to background (value 0), followed by applying a bounding box to the image. Post-processing for both tasks included removing tumor predictions smaller than 175\u2013200\u00a0mm<jats:sup>3<\/jats:sup> and node predictions under 50\u201360\u00a0mm<jats:sup>3<\/jats:sup>. Our models achieved testing DSCagg scores of 0.72 and 0.82 for GTVn and GTVp in Task 1 (pre-RT MRI) and testing DSCagg scores of 0.81 and 0.49 for GTVn and GTVp in Task 2 (mid-RT MRI). This study underscores the feasibility and promise of deep learning-based auto-segmentation for improving clinical workflows in radiation oncology, particularly in adaptive radiotherapy. Future efforts will focus on refining mid-RT segmentation performance and further investigating the clinical implications of automated tumor delineation.<\/jats:p>","DOI":"10.1007\/978-3-031-83274-1_21","type":"book-chapter","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T12:42:29Z","timestamp":1740919349000},"page":"259-273","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Ensemble Deep Learning Models for Automated Segmentation of Tumor and Lymph Node Volumes in Head and Neck Cancer Using Pre- and Mid-Treatment MRI: Application of Auto3DSeg and SegResNet"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1713-9538","authenticated-orcid":false,"given":"Dominic","family":"LaBella","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"issue":"17","key":"21_CR1","doi-asserted-by":"publisher","first-page":"3081","DOI":"10.1200\/JCO.22.02625","volume":"41","author":"AK Chaturvedi","year":"2023","unstructured":"Chaturvedi, A.K., et al.: Human papillomavirus and rising oropharyngeal cancer incidence in the United States. 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