Multi-organ segmentation using vantage point forests and binary context features

Dense segmentation of large medical image volumes using a labelled training dataset requires strong classifiers. Ensembles of random decision trees have been shown to achieve good segmentation accuracies with very fast computation times. However,smaller anatomical structures such as muscles or organs with high shape variability present a challenge to them,especially when relying on axis-parallel split functions,which make finding joint relations among features difficult. Recent work has shown that structural and contextual information can be well captured using a large number of simple pairwise intensity comparisons stored in binary vectors. In this work,we propose to overcome current limitations of random forest classifiers by devising new decision trees,which use the entire feature vector at each split node and may thus be able to find representative patterns in high-dimensional feature spaces. Our approach called vantage point forests is related to cluster trees that have been successfully applied to space partitioning. It can be further improved by discarding training samples with a large Hamming distance compared to the test sample. Our method achieves state-of-the-art segmentation accuracy of ≥90% Dice for liver and kidneys in abdominal CT,with significant improvements over random forest,in under a minute.