Patient-specific neural networks for contour propagation in online adaptive radiotherapy

Abstract

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 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.

Dosimetric comparison of autocontouring techniques for online adaptive proton therapy

“…The comparison includes four methods to automatically contour the daily CT. They are only shortly summarized hereafter, as these methods were described in detail previously (Smolders et al 2023).…”

“…The dose difference is also small for some automatic OAR contours that were not geometrically accurate. For example, the median esophagus dice was only 0.69 for RR (Smolders et al 2023), but the corresponding median DD2 is 3.6%. This implies that approximate contours are often sufficient for reoptimization.…”

“…For the target itself, the differences are still much smaller than with non-adapted therapy, highlighting the advantage of adaptation even when the target is automatically delineated. For one patient in the NSCLC data, the PSS performs very badly, due to significant anatomical deformation of the tumor (see Smolders et al (2023)). The Wilcoxon rank test found however that adapting still results in significantly lower DD2 than not adapting (results not shown).…”

“…Online adaptation requires OAR and target contouring on the new images, plan reoptimization and quality assurance (QA). The most time-consuming adaptation process is contouring (Lim-Reinders et al 2017), which can be sped up by automation in several ways, such as rigid registration (RR), deformable registration (DIR), deep learning based autosegmentation or patient-specific segmentation (PSS) (Smolders et al 2023). A recent comparison found that DIR and PSS reached the highest accuracy for a large set of OARs and target volumes for patients with head-and-neck and lung cancer (Smolders et al 2023).…”

“…The most time-consuming adaptation process is contouring (Lim-Reinders et al 2017), which can be sped up by automation in several ways, such as rigid registration (RR), deformable registration (DIR), deep learning based autosegmentation or patient-specific segmentation (PSS) (Smolders et al 2023). A recent comparison found that DIR and PSS reached the highest accuracy for a large set of OARs and target volumes for patients with head-and-neck and lung cancer (Smolders et al 2023). The evaluation was however limited to geometrical measures such as dice score and Hausdorff distance (Taha and Hanbury 2015).…”