Convolutions, Transformers, and their Ensembles for the Segmentation of Organs at Risk in Radiation Treatment of Cervical Cancer

• URL: http://arxiv.org/abs/2303.11501v1
• Date: Mon, 20 Mar 2023 23:44:35 GMT
• Title: Convolutions, Transformers, and their Ensembles for the Segmentation of Organs at Risk in Radiation Treatment of Cervical Cancer
• Authors: Vangelis Kostoulas, Peter A.N. Bosman, and Tanja Alderliesten
• Abstract summary: We will answer the question for the task of segmentation of the Organs At Risk (OARs) in radiation treatment of cervical cancer. We compare several state-of-the-art models belonging to different architecture, as well as a few new models that combine aspects of several state-of-the-art models. We visualize model predictions, create all possible ensembles of models by averaging their output probabilities, and calculate the Dice Coefficient between predictions of models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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