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Partial nephrectomy (PN) is common surgery in urology. Digitization of renalanatomies brings much help to many computer-aided diagnosis (CAD) techniquesduring PN. However, the manual delineation of kidney vascular system and tumoron each slice is time consuming, error-prone, and inconsistent. Therefore, weproposed an entire renal anatomies extraction method from Computed TomographicAngiographic (CTA) images fully based on deep learning. We adopted acoarse-to-fine workflow to extract target tissues: first, we roughly locatedthe kidney region, and then cropped the kidney region for more detailextraction. The network we used in our workflow is based on 3D U-Net. Todealing with the imbalance of class contributions to loss, we combined the diceloss with focal loss, and added an extra weight to prevent excessive attention.We also improved the manual annotations of vessels by merging semi-trainedmodel's prediction and original annotations under supervision. We performedseveral experiments to find the best-fitting combination of variables fortraining. We trained and evaluated the models on our 60 cases dataset with 3different sources. The average dice score coefficient (DSC) of kidney, tumor,cyst, artery, and vein, were 90.9%, 90.0%, 89.2%, 80.1% and 82.2% respectively.Our modulate weight and hybrid strategy of loss function increased the averageDSC of all tissues about 8-20%. Our optimization of vessel annotation improvedthe average DSC about 1-5%. We proved the efficiency of our network on renalanatomies segmentation. The high accuracy and fully automation make it possibleto quickly digitize the personal renal anatomies, which greatly increases thefeasibility and practicability of CAD application on urology surgery.

An Entire Renal Anatomy Extraction Network for Advanced CAD During Partial Nephrectomy