Reducing the training set using semi-supervised self-training algorithm for segmenting the left ventricle in ultrasound images

Abstract

Statistical pattern recognition models are one of the core research topics in the segmentation of the left ventricle of the heart from ultrasound data. The underlying statistical model usually relies on a strong prior for the shape and appearance of the left ventricle whose parameters can be learned using a manually segmented data set. Unfortunately, this is usually quite complex, requiring a large number of parameters that can be robustly learned only if the training set is sufficiently large. The difficulty in obtaining large training sets is currently a major roadblock for the further exploration of statistical models in medical image analysis. In this paper, we present a novel semi-supervised self-training model that reduces the need of large training sets for estimating the parameters of statistical models. This model is initially trained with a small set of manually segmented images, and for each new test sequence, the system re-estimates the model parameters incrementally without any further manual intervention. We show that state-of-the-art segmentation results can be achieved with training sets containing 50 annotated examples.