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Outlining radiosensitive organs on images used to assist radiation therapy (radiotherapy) of patients with head and neck cancer (HNC) is a time\u2010consuming task, in which variability between observers may directly impact on patient treatment outcomes. Auto\u2010segmentation on computed tomography imaging has been shown to result in significant time reductions and more consistent outlines of the organs at risk.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Three convolutional neural network (CNN)\u2010based auto\u2010segmentation architectures were developed using manual segmentations and T2\u2010weighted MRI images provided from the American Association of Physicists in Medicine (AAPM) radiotherapy MRI auto\u2010contouring (RT\u2010MAC) challenge dataset (<jats:italic>n<\/jats:italic>\u00a0=\u00a031). Auto\u2010segmentation performance was evaluated with segmentation similarity and surface distance metrics on the RT\u2010MAC dataset with institutional manual segmentations (<jats:italic>n<\/jats:italic>\u00a0=\u00a010). The generalizability of the auto\u2010segmentation methods was assessed on an institutional MRI dataset (<jats:italic>n<\/jats:italic>\u00a0=\u00a010).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Auto\u2010segmentation performance on the RT\u2010MAC images with institutional segmentations was higher than previously reported MRI methods for the parotid glands (Dice: 0.860 \u00b1 0.067, mean surface distance [MSD]: 1.33 \u00b1 0.40\u00a0mm) and the first report of MRI performance for submandibular glands (Dice: 0.830 \u00b1 0.032, MSD: 1.16 \u00b1 0.47\u00a0mm). We demonstrate that high\u2010resolution auto\u2010segmentations with improved geometric accuracy can be generated for the parotid and submandibular glands by cascading a localizer CNN and a cropped high\u2010resolution CNN. Improved MSDs were observed between automatic and manual segmentations of the submandibular glands when a low\u2010resolution auto\u2010segmentation was used as prior knowledge in the second\u2010stage CNN. Reduced auto\u2010segmentation performance was observed on our institutional MRI dataset when trained on external RT\u2010MAC images; only the parotid gland auto\u2010segmentations were considered clinically feasible for manual correction (Dice: 0.775 \u00b1 0.105, MSD: 1.20 \u00b1 0.60\u00a0mm).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This work demonstrates that CNNs are a suitable method to auto\u2010segment the parotid and submandibular glands on MRI images of patients with HNC, and that cascaded CNNs can generate high\u2010resolution segmentations with improved geometric accuracy. Deep learning methods may be suitable for auto\u2010segmentation of the parotid glands on T2\u2010weighted MRI images from different scanners, but further work is required to improve the performance and generalizability of these methods for auto\u2010segmentation of the submandibular glands and lymph nodes.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15290","type":"journal-article","created":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T07:35:05Z","timestamp":1634888105000},"page":"7757-7772","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Cascaded deep learning\u2010based auto\u2010segmentation for head and neck cancer patients: Organs at risk on T2\u2010weighted magnetic resonance imaging"],"prefix":"10.1002","volume":"48","author":[{"given":"James C.","family":"Korte","sequence":"first","affiliation":[{"name":"Department of Physical Science Peter MacCallum Cancer Centre  Melbourne Victoria 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