Planning of radiotherapy involves accurate segmentation of a large number of organs at risk, i.e. organs for which irradiation doses should be minimized to avoid important side effects of the therapy. We propose a deep learning method for segmentation of organs at risk inside the brain region, from Magnetic Resonance (MR) images. Our system performs segmenta-tion of eight structures: eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem and brain. We propose an efficient algorithm to train neural networks for an end-to-end segmentation of multiple and non-exclusive classes, addressing problems related to computational costs and missing ground truth segmentations for a subset of classes. We enforce anatomical consistency of the result in a postprocessing step, in particular we introduce a graph-based algorithm for segmentation of the optic nerves, enforcing the connectivity between the eyes and the optic chiasm. We report cross-validated quantitative results on a database of 44 contrast-enhanced T1-weighted MRIs with provided segmentations of the considered organs at risk, which were originally used for radiotherapy planning. In addition, the segmentations produced by our model on an independent test set of 50 MRIs are evaluated by an experienced radiotherapist in order to qualitatively assess their accuracy. The mean distances between produced segmentations and the ground truth ranged from 0.1 mm to 0.7 mm across different organs. A vast majority (96 ) of the produced segmentations were found acceptable for radiotherapy planning.
Most of the proposed deep learning methods for segmentation of organs at risk were applied on CT scans in the context of head and neck cancers @cite_45 , i.e. cancers of the upper parts of respiratory and digestive systems (mouth, larynx, throat). To the best of our knowledge, the only deep learning method for segmentation of organs at risk in MRIs of the brain is the one proposed in @cite_33 (MRI T1 and T2).
@cite_45 @cite_33
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Summary Most head and neck cancers are squamous cell carcinomas that develop in the upper aerodigestive epithelium after exposure to carcinogens such as tobacco and alcohol. Human papillomavirus has also been strongly implicated as a causative agent in a subset of these cancers. The complex anatomy and vital physiological role of the tumour-involved structures dictate that the goals of treatment are not only to improve survival outcomes but also to preserve organ function. Major improvements have been accomplished in surgical techniques and radiotherapy delivery. Moreover, systemic therapy including chemotherapy and molecularly targeted agents—namely, the epidermal growth factor receptor inhibitors—has been successfully integrated into potentially curative treatment of locally advanced squamous-cell carcinoma of the head and neck. In deciding which treatment strategy would be suitable for an individual patient, important considerations include expected functional outcomes, ability to tolerate treatment, and comorbid illnesses. The collaboration of many specialties is the key for optimum assessment and decision making. We review the epidemiology, molecular pathogenesis, diagnosis and staging, and the latest multimodal management of squamous cell carcinoma of the head and neck. Organ-at-risk (OAR) segmentation is a key step for radiotherapy treatment planning. Model-based segmentation (MBS) has been successfully used for the fully automatic segmentation of anatomical structures and it has proven to be robust to noise due to its incorporated shape prior knowledge. In this work, we investigate the advantages of combining neural networks with the prior anatomical shape knowledge of the model-based segmentation of organs-at-risk for brain radiotherapy (RT) on Magnetic Resonance Imaging (MRI). We train our boundary detectors using two different approaches: classic strong gradients as described in [4] and as a locally adaptive regression task, where for each triangle a convolutional neural network (CNN) was trained to estimate the distances between the mesh triangles and organ boundary, which were then combined into a single network, as described by [1]. We evaluate both methods using a 5-fold cross-validation on both T1w and T2w brain MRI data from sixteen primary and metastatic brain cancer patients (some post-surgical). Using CNN-based boundary detectors improved the results for all structures in both T1w and T2w data. The improvements were statistically significant ( (p<0.05 )) for all segmented structures in the T1w images and only for the auditory system in the T2w images.