Guided Random Forests for identification of key fetal anatomy and image categorization in ultrasound scans

Abstract

In this paper, we propose a novel machine learning based method to categorize unlabeled fetal ultrasound images. The proposed method guides the learning of a Random Forests classifier to extract features from regions inside the images where meaningful structures exist. The new method utilizes a translation and orientation invariant feature which captures the appearance of a region at multiple spatial resolutions. Evaluated on a large real world clinical dataset (~30K images from a hospital database), our method showed very promising categorization accuracy (accuracytop1 is 75% while accuracytop2 is 91%).