Speaker 1: Cedric Rodriguez
Title: Bi-objective optimization of organ properties for the simulation of intracavitary brachytherapy applicator placement in cervical cancer
Abstract: Validation of deformable image registration techniques is extremely important, but hard, especially when complex deformations or content mismatch are involved. These complex deformations and content mismatch, for example, occur after the placement of an applicator for brachytherapy for cervical cancer. Virtual phantoms could enable the creation of validation data sets with ground truth deformations that simulate the large deformations that occur between image acquisitions. However, the quality of the multi-organ Finite Element Method (FEM)-based simulations is dependent on the patient-specific external forces and mechanical properties assigned to the organs. A common approach to calibrate these simulation parameters is through optimization, finding the parameter settings that optimize the match between the outcome of the simulation and reality. When considering inherently simplified organ models, we hypothesize that the optimal deformations of one organ cannot be achieved with a single parameter setting without compromising the optimality of the deformation of the surrounding organs. This means that there will be a trade-off between the optimal deformations of adjacent organs, such as the vagina-uterus and bladder. This work therefore proposes and evaluates a multi-objective optimization approach where the trade-off between organ deformations can be assessed after optimization. We showcase what the extent of the trade-off looks like when bi-objectively optimizing the patient-specific mechanical properties and external forces of the vagina-uterus and bladder for FEM-based simulations.
Speaker 2: Vangelis Kostoulas
Title: Convolutions, Transformers, and their Ensembles for the Segmentation of Organs at Risk in Radiation Treatment of Cervical Cancer
Abstract: Segmentation of regions of interest in images of patients, is a crucial step in many medical procedures. Deep neural networks have proven to be particularly adept at this task. However, a key question is what type of deep neural network to choose, and whether making a certain choice makes a difference. In this work, we will answer this question for the task of segmentation of the Organs At Risk (OARs) in radiation treatment of cervical cancer (i.e., bladder, bowel, rectum, sigmoid) in Magnetic Resonance Imaging (MRI) scans. We compare several state-of-the-art models belonging to different architecture categories, as well as a few new models that combine aspects of several state-of-the-art models, to see if the results one gets are markedly different. We visualize model predictions, create all possible ensembles of models by averaging their output probabilities, and calculate the Dice Coefficient between predictions of models, in order to understand the differences between them and the potential of possible combinations. The results show that small improvements in metrics can be achieved by advancing and merging architectures, but the predictions of the models are quite similar (most models achieve on average more than 0.8 Dice Coefficient when compared to the outputs of other models). However, the results from the ensemble experiments indicate that the best results are obtained when the best performing models from every category of the architectures are combined.
