SEMANTIC ORGAN SEGMENTATION IN 3D WHOLE-BODY MR IMAGES

Automated organ segmentation is a prerequisite for efficient analysis of MR data in large cohorts with thousands of participants. The feasibility and generalizability of previously proposed methods has mostly been demonstrated in smaller cohorts. The aim of this work is to implement and validate automated semantic 3D segmentation of liver and spleen on multi-contrast MR data of the body trunk which were acquired in a large epidemiological imaging study with the objective to provide a robust and general setup in a setting of limited training data. Liver and spleen were manually segmented in 173 MR images by an experienced radiologist, providing labeled ground-truth. Varying amount of training datasets were randomly chosen to train a convolutional neural network (CNN)-based segmentation with 4-fold patient-leave-out cross-validation and compared against a Random Forest (RF)-based segmentation. Validation amongst participants revealed high accuracies of 99.7%/99.9% for liver/spleen-segmentation with superiority of CNN to RF. In conclusion, automated semantic organ segmentation is feasible in a robust and general setup.