Segmentation of Renal Structures for Image-Guided Surgery.

Anatomic models of kidneys may help surgeons make plans or guide surgical procedures, in which segmentation is a prerequisite. We develop a convolutional neural network to segment multiple renal structures from arterial-phase CT images, including parenchyma, arteries, veins, collecting systems, and abnormal structures. To the best of our knowledge, this is the first work dedicated to jointly segment these five renal structures. We introduce two novel techniques. First, we generalize the sequential residual architecture to residual graphs. With this generalization, we convert a popular multi-scale architecture (U-Net) to a residual U-Net. Second, we solve the unbalanced data problem which commonly exists in medical image segmentation by weighting pixels with multi-scale entropy. Our multi-scale entropy map combines information theory and scale analysis to capture spatial complexity of a multi-class label map. The two techniques significantly improve segmentation accuracy. Trained on 400 CT scans and tested on another 100, our algorithm achieves median Dice indices 0.96, 0.86, 0.8, 0.62, and 0.29 respectively for renal parenchyma, arteries, veins, collecting systems and abnormal structures.