2015/04/15 by Holger R. Roth, Christopher T. Lee, Hoo-Chang Shin +5 · 1 citation
Computer Science · #cs.CV
paper · pdf · doi:10.1109/isbi.2015.7163826
published as Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on Year: 2015 Pages: 101 - 104 · Presented at: 2015 IEEE International Symposium on Biomedical Imaging, April 16-19, 2015, New York Marriott at Brooklyn Bridge, NY, USA
arxiv created 2015/04/15 · arxiv updated 2015/09/17
Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. "Deep learning" methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.