Abstract #2296

Segmentation of Liver Lesions in WB-DWI Using Deformable Registration and Machine Learning Techniques

Annemarie Knill^1,2, Antonio Candito¹, Jessica Winfield^1,2, James Larkin^1,2, Samra Turajlic^2,3, Dow Mu Koh^1,2, Christina Messiou^1,2, and Matthew Blackledge¹

¹The Institute of Cancer Research, London, United Kingdom, ²The Royal Marsden NHS Foundation Trust, London, United Kingdom, ³The Francis Crick Institute, London, United Kingdom

Synopsis

We present a proof-of-concept study to assess whether deformable registration followed by tissue classification using machine learning (ML) is an effective method for the delineation of liver metastases in whole-body diffusion-weighted imaging (WB-DWI). Deformable atlas-based registration achieves good quality delineation of the liver (Dice coefficient > 70%) and out of three ML models random forest achieved the best F-1 measure for segmenting disease within the liver.

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