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Abstract #3484

Domain adaptation for prostate lesion segmentation on VERDICT-MRI

Eleni Chiou1,2, Francesco Giganti3,4, Shonit Punwani5, Iasonas Kokkinos2, and Eleftheria Panagiotaki1,2
1Centre of Medical Imaging Computing, University College London, London, United Kingdom, 2Department of Computer Science, University College London, London, United Kingdom, 3Department of Radiology, UCLH NHS Foundation Trust, University College London, London, United Kingdom, 4Division of Surgery & Interventional Science, University College London, London, United Kingdom, 5Centre for Medical Imaging, Division of Medicine, University College London, London, United Kingdom

The successful adoption of convolutional neural networks (CNNs) for improved diagnosis can be hindered for pathologies and clinical settings where the amount of labelled training data is limited. In such cases, domain adaptation provides a viable alternative. In this work we propose domain adaptation to enhance the performance of prostate lesion segmentation on VERDICT-MRI utilising diffusion weighted (DW)-MRI data from multi-parametric (mp)-MRI acquisitions. Experimental results show that domain adaptation significantly improves the segmentation performance on VERDICT-MRI.

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