Abstract #3484

Domain adaptation for prostate lesion segmentation on VERDICT-MRI

Eleni Chiou^1,2, Francesco Giganti^3,4, Shonit Punwani⁵, Iasonas Kokkinos², and Eleftheria Panagiotaki^1,2

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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