Semi-supervised self-training model for the segmentation of the left ventricle of the heart from ultrasound data

Abstract

The design and use of statistical pattern recognition models can be regarded as one of the core research topics in the segmentation of the left ventricle of the heart from ultrasound data. These models trade a strong prior model of the shape and appearance of the left ventricle for a statistical model whose parameters can be learned using a manually segmented data set (this set is commonly known as the training set). The trouble is that such statistical model 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 problems, such as the automatic left ventricle segmentation. 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 reestimates 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 for the problem of left ventricle segmentation from ultrasound data.