Convolutional networks for kidney segmentation in contrast-enhanced CT scans
William Thong, Samuel Kadoury, Nicolas Piché, Christopher Pal · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · 2016
Organ segmentation in medical imagery can be used to guide patient diagnosis, treatment and follow ups. In this paper, we present a fully automatic framework for kidney segmentation with convolutional networks (ConvNets) in contrast-enhanced computerised tomography (CT) scans. In our approach, a ConvNet is trained using a patch-wise approach to predict the class membership of the central voxel in 2D patches. The segmentation of the kidneys is then produced by densely running the ConvNet over each slice of a CT scan. Efficient predictions can be achieved by transforming fully connected layers into convolutional operations and by fragmenting the maxpooling layers to segment a whole CT scan volume in a few seconds. We report the segmentation performance of our framework on a highly variable data-set of 79 cases using a variety of evaluation metrics.