Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning

This study is focused on developing an automated algorithm for the detection and segmentation of Abdominal Aortic Aneurysm (AAA) region in CT Angiography images. The outcome of this research will offer great assistance for radiologists to detect the AAA region and estimate its volume in CT datasets efficiently. In addition, suitable treatment strategies can also be suggested based on the classification of the AAA severity and measurement of the aorta diameter. This research takes the initiative by exploring and applying deep learning architecture in the study of AAA detection and segmentation, which has never been done by other researchers before in AAA problems. The findings from this study will also act as a reference for other similar future works. Deep Belief Network (DBN) is applied for the purpose of AAA detection and severity classification in this study. Optimum parameters for training the DBN are determined for the training data from the selected dataset. AAA region can be successfully segmented from the CT images and the result is comparable to the existing method with advantage over the existing method that the proposed method is fully automatic and added with auto detection and classification features. The limitation of the trained DBN in AAA detection accuracy can be improved by incorporating and adjusting the detection probability threshold and shape constraints. In future, the DBN can be further enhanced by adding and training it with more data which covers a wider variety of features, as well as extending its capability to perform detailed segmentation on AAA region

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 and segmentation more commonly found in the literature. Another innovation of our approach is that we use a statistical model based on deep learning architectures, which are easily adapted to this on-line incremental learning framework. We show that our fully automatic LV segmentation method achieves state-of-the-art accuracy with training sets containing less than twenty annotated images.Gustavo Carneiro, Jacinto C. Nascimentohttp://www.iccv2011.org