Outlining radiosensitive organs on images used to assist radiotherapy of patients with head and neck cancer (HNC) is a time consuming task [1], in which variability between observers may directly impact on patient treatment outcomes [2,3]. Automated segmentation (auto-segmentation) on computed tomography imaging has been show to result in significant time reductions [4] and more consistent outlines of the organs at risk [5]. Application of these methods to MRI would be beneficial in an oncology setting with an increasing use of magnetic resonance imaging (MRI)...

The AAPM RT-MAC challenge dataset [6], which consists of T2 weighted MRI and manual segmentations of HNC organs at risk, was used to develop three convolution neural network (CNN) auto-segmentation architectures (see Figure 1.); a low-resolution method, a cascaded high-resolution method and a cascaded high-resolution method with prior-knowledge. [Fig 1] The performance of the three auto-segmentation methods was compared using segmentation similarity and surface distance metrics on the RT-MAC images with an institutional manual segmentation set. An institutional MRI and manual segmentation dataset was used...

[Fig 2] The parotid gland auto-segmentation performance (dice: 0.860±0.067, mean surface distance: 1.33±0.40 mm) on the RT-MAC images with institutional segmentations was higher than previously reported MRI methods [7-9]. An example of auto-segmentations from each of the network architectures is shown in Figure 2. The submandibular gland performance (dice: 0.830±0.032, mean surface distance: 1.16±0.47 mm) is the first reported on MRI images. In the comparison of CNN architectures we demonstrate that by cascading a localiser CNN with a cropped high-resolution CNN it is possible to...

This study demonstrates that deep learning methods may be suitable for auto-segmentation of the parotid glands on T2 weighted MRI images from different scanners. Further work is required to improve the performance and generalisability of deep learning based methods for auto-segmentation of the submandibular glands and lymph nodes.

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