On-line re-training and segmentation with reduction of the training set: application to the left ventricle detection in ultrasound imaging

Abstract

The segmentation of the left ventricle (LV) still constitutes an active research topic in medical image processing field. The problem is usually tackled using pattern recognition methodologies. The main difficulty with pattern recognition methods is its dependence of a large manually annotated training sets for a robust learning strategy. However, in medical imaging, it is difficult to obtain such large annotated data. In this paper, we propose an on-line semi-supervised algorithm capable of reducing the need of large training sets. The main difference regarding semi-supervised techniques is that, the proposed framework provides both an on-line retraining and segmentation, instead of on-line retraining and offline segmentation. Our proposal is applied to a fully automatic LV segmentation with substantially reduced training sets while maintaining good segmentation accuracy.