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Tumor delineation is used throughout radiotherapy for treatment planning, initially for pre-radiotherapy (pre-RT) MRI scans followed-up by mid-radiotherapy (mid-RT) during the treatment. For the pre-RT task, we propose a dual-stage 3D UNet approach using cascaded neural networks for progressive accuracy refinement. The first-stage models produce an initial binary segmentation, which is then refined with an ensemble of second-stage models for a multiclass segmentation. In Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Task 1, we utilize a dataset consisting of pre-RT and mid-RT T2-weighted MRI scans. The method is trained using 5-fold cross-validation and evaluated as an ensemble of five coarse models and ten refinement models. Our approach (team FinoxyAI) achieves a mean aggregated Dice similarity coefficient of 0.737 on the test set. Moreover, with this metric, our dual-stage approach highlights consistent improvement in segmentation performance across all folds compared to a single-stage segmentation method.<\/jats:p>","DOI":"10.1007\/978-3-031-83274-1_14","type":"book-chapter","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T12:42:40Z","timestamp":1740919360000},"page":"191-203","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Head and\u00a0Neck Tumor Segmentation Using Pre-RT MRI Scans and\u00a0Cascaded DualUNet"],"prefix":"10.1007","author":[{"given":"Mikko","family":"Saukkoriipi","sequence":"first","affiliation":[]},{"given":"Jaakko","family":"Sahlsten","sequence":"additional","affiliation":[]},{"given":"Joel","family":"Jaskari","sequence":"additional","affiliation":[]},{"given":"Ahmed","family":"Al-Tahmeesschi","sequence":"additional","affiliation":[]},{"given":"Laura","family":"Ruotsalainen","sequence":"additional","affiliation":[]},{"given":"Kimmo","family":"Kaski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Thiagarajan, A., et al.: Target volume delineation in oropharyngeal cancer: impact of PET, MRI, and physical examination. 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