  Partial nephrectomy (PN) is common surgery in urology. Digitization of renal
anatomies brings much help to many computer-aided diagnosis (CAD) techniques
during PN. However, the manual delineation of kidney vascular system and tumor
on each slice is time consuming, error-prone, and inconsistent. Therefore, we
proposed an entire renal anatomies extraction method from Computed Tomographic
Angiographic (CTA) images fully based on deep learning. We adopted a
coarse-to-fine workflow to extract target tissues: first, we roughly located
the kidney region, and then cropped the kidney region for more detail
extraction. The network we used in our workflow is based on 3D U-Net. To
dealing with the imbalance of class contributions to loss, we combined the dice
loss with focal loss, and added an extra weight to prevent excessive attention.
We also improved the manual annotations of vessels by merging semi-trained
model's prediction and original annotations under supervision. We performed
several experiments to find the best-fitting combination of variables for
training. We trained and evaluated the models on our 60 cases dataset with 3
different 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 average
DSC of all tissues about 8-20%. Our optimization of vessel annotation improved
the average DSC about 1-5%. We proved the efficiency of our network on renal
anatomies segmentation. The high accuracy and fully automation make it possible
to quickly digitize the personal renal anatomies, which greatly increases the
feasibility and practicability of CAD application on urology surgery.
