A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning

Objective: Manual contouring and registration for radiotherapy treatment planning and online adaptation for cervical cancer radiation therapy in computed tomography (CT) and magnetic resonance images (MRI) are often necessary. However manual intervention is time consuming and may suffer from inter or intra-rater variability. In recent years a number of computer-guided automatic or semi-automatic segmentation and registration methods have been proposed. Segmentation and registration in CT and MRI for this purpose is a challenging task due to soft tissue deformation, inter-patient shape and appearance variation and anatomical changes over the course of treatment. The objective of this work is to provide a state-of-the-art review of computer-aided methods developed for adaptive treatment planning and radiation therapy planning for cervical cancer radiation therapy. Methods: Segmentation and registration methods published with the goal of cervical cancer treatment planning and adaptation have been identified from the literature (PubMed and Google Scholar). A comprehensive description of each method is provided. Similarities and differences of these methods are highlighted and the strengths and weaknesses of these methods are discussed. A discussion about choice of an appropriate method for a given modality is provided. Results: In the reviewed papers a Dice similarity coefficient of around 0.85 along with mean absolute surface distance of 2-4. mm for the clinically treated volume were reported for transfer of contours from planning day to the treatment day. Conclusions: Most segmentation and non-rigid registration methods have been primarily designed for adaptive re-planning for the transfer of contours from planning day to the treatment day. The use of shape priors significantly improved segmentation and registration accuracy compared to other models

Automated Online-Adaptive Intensity-Modulated Proton Therapy

In this thesis I developed algorithms to adapt a proton therapy irradiation plan automatically and rapidly to the patient anatomy of the day. This saves healthy tissue better, which is expected to lead to fewer side effects

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy

Combined Ultrasound and Cone Beam CT Improves Target Segmentation for Image Guided Radiation Therapy in Uterine Cervix Cancer.

Purpose Adaptive radiation therapy strategies could account for interfractional uterine motion observed in patients with cervix cancer, but the current cone beam computed tomography (CBCT)-based treatment workflow is limited by poor soft-tissue contrast. The goal of the present study was to determine if ultrasound (US) could be used to improve visualization of the uterus, either as a single modality or in combination with CBCT.Methods and materials Interobserver uterine contour agreement and confidence were compared on 40 corresponding CBCT, US, and CBCT-US-fused images from 11 patients with cervix cancer. Contour agreement was measured using the Dice similarity coefficient (DSC) and mean contour-to-contour distance (MCCD). Observers rated their contour confidence on a scale from 1 to 10. Pairwise Wilcoxon signed-rank tests were used to measure differences in contour agreement and confidence.Results CBCT-US fused images had significantly better contour agreement and confidence than either individual modality (P < .05), with median (interquartile range [IQR]) values of 0.84 (0.11), 1.26 (0.23) mm, and 7 (2) for the DSC, MCCD, and observer confidence ratings, respectively. Contour agreement was similar between US and CBCT, with median (IQR) DSCs of 0.81 (0.17) and 0.82 (0.14) and MCCDs of 1.75 (1.15) mm and 1.62 (0.74) mm. Observers were significantly more confident in their US-based contours than in their CBCT-based contours (P < .05), with median (IQR) confidence ratings of 7 (2.75) versus 5 (4).Conclusions CBCT and US are complementary and improve uterine segmentation precision when combined. Observers could localize the uterus with a similar precision on independent US and CBCT images