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Abstract #2164

A deep-learning non-contrast MRI lesion segmentation model for liver cancer patients that may have had previous surgery

Nora Vogt1, Zobair Arya1, Luis Núñez1, John Connell1, and Paul Aljabar1
1Perspectum, Oxford, United Kingdom


Segmentation of lesions within the liver is pivotal for surveillance, diagnosis and treatment planning, and automated or semi-automated approaches can aid clinical workflows. Many patients that are under surveillance may have had previous surgery, meaning their scans will contain post-surgical features. We investigate the use of a deep learning segmentation model for non-contrast MRI that can distinguish between lesions, surgical clips and resection-induced fluid-filling regions. We report mean dice scores of 0.55, 0.72 and 0.88 for these classes respectively, demonstrating the potential of this model for semi-automated workflows.

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