Registration-Guided Deep Learning Image Segmentation for Cone Beam CT-based Online Adaptive Radiotherapy

TL;DR

This paper introduces a registration-guided deep learning framework for accurate, fast segmentation of organs at risk in CBCT images, enhancing online adaptive radiotherapy by combining image registration with deep learning.

Contribution

The study presents a novel registration-guided deep learning framework that integrates registration algorithms with DL models, significantly improving segmentation accuracy and speed for online ART applications.

Findings

RgDL outperforms baseline methods in segmentation accuracy.

Rig-RgDL achieves a mean DSC of 84.5%, surpassing individual methods.

Inference time is less than one second for segmenting seven organs.

Abstract

Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such as CBCT, to meet the online demands of plan evaluation and adaptation. Deep learning (DL)-based automatic segmentation has gained great success in segmenting planning CT, but its applications to CBCT yielded inferior results due to the low image quality and limited available contour labels for training. To overcome these obstacles to online CBCT segmentation, we propose a registration-guided DL (RgDL) segmentation framework that integrates image registration algorithms and DL segmentation models. The registration algorithm generates initial contours, which were used as guidance by DL model to obtain accurate final segmentations. We had two implementations the proposed framework--Rig-RgDL (Rig for rigid body) and Def-RgDL (Def for deformable)--with rigid body (RB) registration or deformable image registration (DIR) as the registration algorithm respectively and U-Net as DL model architecture. The two implementations of RgDL framework were trained and evaluated on seven OARs in an institutional clinical Head and Neck (HN) dataset. Compared to the baseline approaches using the registration or the DL alone, RgDL achieved more accurate segmentation, as measured by higher mean Dice similarity coefficients (DSC) and other distance-based metrics. Rig-RgDL achieved a DSC of 84.5% on seven OARs on average, higher than RB or DL alone by 4.5% and 4.7%. The DSC of Def-RgDL is 86.5%, higher than DIR or DL alone by 2.4% and 6.7%. The inference time took by the DL model to generate final segmentations of seven OARs is less than one second in RgDL. The resulting segmentation accuracy and efficiency show the promise of applying RgDL framework for online ART.