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Prostate cancer and benign prostatic hyperplasia are common diseases in men and require early and accurate diagnosis for optimal treatment. Standard diagnostic tests such as the prostate-specific antigen test and digital rectal examination are inconvenient. Thus, non-invasive methods such as magnetic resonance imaging (MRI) and automated image analysis are increasingly utilised to facilitate and improve prostate diagnostics. Segmentation is a vital part of the prostate image analysis pipeline, and deep neural networks are now the tool of choice to automate this task. In this work, we benchmark various deep neural networks for 3D prostate segmentation using four different publicly available datasets and one private dataset. We show that popular networks such as U-Net trained on one dataset typically generalise poorly when tested on others due to data heterogeneity. Aiming to address this issue, we propose a novel deep-learning architecture for prostate whole-gland segmentation in T2-weighted MRI images that exploits various techniques such as pyramid pooling, concurrent spatial and channel squeeze and excitation, and deep supervision. Our extensive experiments demonstrate that it performs superiorly without requiring special adaptation to any specific dataset.
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Wednesday 6th July
Poster Session 1.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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