Introduction
deformations
CERVICAL CANCER
Cervical cancer is one the most common cause of cancer-related female mortality in the world [Recently, deep learning approaches, including supervised, unsupervised, and semi-supervised methods, are employed in different medical image analysis tasks [Alternatively, unsupervised deep learning algorithm-based solutions are more appealing for this type of challenge. For example, Wu et al. [However, for the DIR problem, a dense flow field ground-truth correspondence is rarely available. Some studies proposed specific frameworks to generate synthesized deformation parameters for the creation of a training dataset for the tasks of rigid registration [In this study, three major issues are addressed: (1) simulation of a realistic local deformation in the image domain that takes into account clinical considerations. As mentioned above, defining a realistic deformation, especially locally and/or organ-wise, would be a challenging task for the creation of the registration ground-truth dataset. In this regard, a novel training data generation framework is proposed in this study, which enables the definition of any desirable deformations depending on the organ rigidity/mobility. (2) Creation of a dataset for DIR problems, which can be used by any machine learning approach. (3) Development of a multi-channel deep neural network to simultaneously process the original CT images together with the binary masks of the OARs.This study proposes a new method for the inter-fraction deformation simulation in the HDR brachytherapy to be used in a deep learning-based DIR algorithm. Furthermore, a deep learning-based DIR algorithm is developed to directly perform the image alignment of the inter-fraction HDR sessions. A deep learning model consisting of five input channels was implemented and dedicated to the CT images of the bladder, cervix, rectum, and bone OARs in the form of binary masks to perform image alignment between two CT slices.