Registration‐guided deep learning image segmentation for cone beam CT–based online adaptive radiotherapy

Abstract

Purpose Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART process is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such as cone beam computed tomography (CBCT). Direct application of deep learning (DL)‐based segmentation to CBCT images suffered from issues such as 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. Methods The RgDL framework is composed of two components: image registration and RgDL segmentation. The image registration algorithm transforms/deforms planning contours that were subsequently used as guidance by the DL model to obtain accurate final segmentations. We had two implementations of 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 the DL model architecture. The two implementations of RgDL framework were trained and evaluated on seven OARs in an institutional clinical head‐and‐neck dataset. Results Compared to the baseline approaches using the registration or the DL alone, RgDLs achieved more accurate segmentation, as measured by higher mean Dice similarity coefficients (DSCs) 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 average DSC of Def‐RgDL was 86.5%, higher than DIR or DL alone by 2.4% and 6.7%. The inference time required by the DL model component to generate final segmentations of seven OARs was less than 1 s in RgDL. By examining the contours from RgDLs and DL case by case, we found that RgDL was less susceptible to image artifacts. We also studied how the performances of RgDL and DL vary with the size of the training dataset. The DSC of DL dropped by 12.1% as the number of training data decreased from 22 to 5, whereas RgDL only dropped by 3.4%. Conclusion By incorporating the patient‐specific registration guidance to a population‐based DL segmentation model, RgDL framework overcame the obstacles associated with online CBCT segmentation, including low image quality and insufficient training data, and achieved better segmentation accuracy than baseline methods. The resulting segmentation accuracy and efficiency show promise for applying this RgDL framework for online ART.

Multi-Modal U-net for Segmenting Gross Tumor Volume in Lungs during Radiotherapy

“…They mentioned that this type of input covered more spatial region than 2D CNN while it had less parameters than 3D CNN. Ma et al [18] proposed a registration-guided deep learning architecture that used CBCT images and registered CT-Masks to delineate Organs at Risk. They used two different types of registration on the CT masks i.e.…”

“…We further perform analysis of different fusion strategies using different types of inaccuracies in the CT-Mask. As mentioned in [18], the OAR and the tumor volume are required to be delineated. As they segment the organs at risk, we go forward to segment the GTV using this approach.…”

“…Though there should be a balance of information, as too much dependence on the mask might affect the overall performance of the model. One of the reasons of using such a method is that CBCT's are only used for image guidance and are rarely delineated manually, and so there exists a problem of limited data [18]. This type of model helps in solving this limitation as it guides the network towards the GTV.…”

Patient specific prior cross attention for kV decomposition in paraspinal motion tracking

“…Priori information has been widely used to enhance performance in solving medical imaging problems, for example, in the fields of image registration, image reconstruction, image segmentation, denoising, image artifact removal, and synthetic image generation. [13][14][15][16][17][18] Traditionally,prior knowledge is often incorporated into a model with an explicit form. For example, an edgepreserving prior took the form of a quadratic penalty or potential function.…”

“…In this work, we introduce a new input of patient‐specific prior to improve the generalizability of our deep‐learning model. Priori information has been widely used to enhance performance in solving medical imaging problems, for example, in the fields of image registration, image reconstruction, image segmentation, denoising, image artifact removal, and synthetic image generation 13–18 …”