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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