Incremental On-line Semi-supervised Learning for Segmenting the Left Ventricle of the Heart from Ultrasound Data

Recently, there has been an increasing interest in the investigation of statistical pattern recognition models for the fully automatic segmentation of the left ventricle (LV) of the heart from ultrasound data. The main vulnerability of these models resides in the need of large manually annotated training sets for the parameter estimation procedure. The issue is that these training sets need to be annotated by clinicians, which makes this training set acquisition process quite expensive. Therefore, reducing the dependence on large training sets is important for a more extensive exploration of statistical models in the LV segmentation problem. In this paper, we present a novel incremental on-line semi-supervised learning model that reduces the need of large training sets for estimating the parameters of statistical models. Compared to other semi-supervised techniques, our method yields an on-line incremental re-training and segmentation instead of the off-line incremental re-training an...

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Gustavo Carneiro, Jacinto C. Nascimento
• Keywords: Computer Vision, ICCV 2011, Segmentation Method, Segmentation Problem, Statistical Pattern Recognition