Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer

Journal of Imaging Informatics in Medicine, vol. 36, pp. 574–587

Abstract

In this study, an inter-fraction organ deformation simulation framework for the locally advanced cervical cancer (LACC), which considers the anatomical flexibility, rigidity, and motion within an image deformation, was proposed. Data included 57 CT scans (7202 2D slices) of patients with LACC randomly divided into the train (n = 42) and test (n = 15) datasets. In addition to CT images and the corresponding RT structure (bladder, cervix, and rectum), the bone was segmented, and the coaches were eliminated. The correlated stochastic field was simulated using the same size as the target image (used for deformation) to produce the general random deformation. The deformation field was optimized to have a maximum amplitude in the rectum region, a moderate amplitude in the bladder region, and an amplitude as minimum as possible within bony structures. The DIRNet is a convolutional neural network that consists of convolutional regressors, spatial transformation, as well as resampling blocks. It was implemented by different parameters. Mean Dice indices of 0.89 ± 0.02, 0.96 ± 0.01, and 0.93 ± 0.02 were obtained for the cervix, bladder, and rectum (defined as at-risk organs), respectively. Furthermore, a mean average symmetric surface distance of 1.61 ± 0.46 mm for the cervix, 1.17 ± 0.15 mm for the bladder, and 1.06 ± 0.42 mm for the rectum were achieved. In addition, a mean Jaccard of 0.86 ± 0.04 for the cervix, 0.93 ± 0.01 for the bladder, and 0.88 ± 0.04 for the rectum were observed on the test dataset (15 subjects). Deep learning-based non-rigid image registration is, therefore, proposed for the high-dose-rate brachytherapy in inter-fraction cervical cancer since it outperformed conventional algorithms.

Authors 6

1. Mohammad Salehi

   Iran University of Medical Sciences

   Affiliation as printed

   Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

2. Alireza Vafaei Sadr Aachen Institute of Pathology

   University of Geneva · Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Theoretical Physics and Center for Astroparticle Physics, University of Geneva, Geneva, Switzerland

   Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany

3. Seied Rabi Mahdavi

   Iran University of Medical Sciences

   Affiliation as printed

   Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

4. Hossein Arabi

   University Hospital of Geneva

   Affiliation as printed

   Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland

5. Isaac Shiri corresponding

   University Hospital of Geneva

   Affiliation as printed

   Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. Isaac.shirilord@unige.ch

   Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland

6. Reza Reiazi corresponding

   The University of Texas MD Anderson Cancer Center · Iran University of Medical Sciences

   Affiliation as printed

   Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. rreiazi@mdanderson.org

   Division of Radiation Oncology, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA. rreiazi@mdanderson.org

   Division of Radiation Oncology, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA