Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (<scp>MRI</scp>), using classification forests (<scp>CF</scp>s), convolutional neural networks (<scp>CNN</scp>s), and a multi‐atlas (<scp>MA</scp>) approach

Abstract

Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance.