Objective.fast and accurate contouring of daily 3D images is a prerequisite for online adaptive radiotherapy. Current automatic techniques rely either on contour propagation with registration or deep learning (DL) based segmentation with convolutional neural networks (CNNs). Registration lacks general knowledge about the appearance of organs and traditional methods are slow. CNNs lack patient-specific details and do not leverage the known contours on the planning computed tomography (CT). This works aims to incorporate patient-specific information into CNNs to improve their segmentation accuracy.Approach.patient-specific information is incorporated into CNNs by retraining them solely on the planning CT. The resulting patient-specific CNNs are compared to general CNNs and rigid and deformable registration for contouring of organs-at-risk and target volumes in the thorax and head-and-neck regions.Results.patient-specific fine-tuning of CNNs significantly improves contour accuracy compared to standard CNNs. The method further outperforms rigid registration and a commercial DL segmentation software and yields similar contour quality as deformable registration (DIR). It is additionally 7-10 times faster than DIR.Significance.patient-specific CNNs are a fast and accurate contouring technique, enhancing the benefits of adaptive radiotherapy.
Keywords: adaptive radiotherapy; biomedical image segmentation; contour propagation; deep learning.
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