# Precise Estimation of Renal Vascular Dominant Regions Using Spatially Aware Fully Convolutional Networks, Tensor-Cut and Voronoi Diagrams

## TL;DR

This paper introduces a fully automatic method combining neural networks, tensor-based graph-cut, and Voronoi diagrams to accurately estimate renal vascular dominant regions, aiding pre-surgical planning for partial nephrectomy.

## Contribution

It proposes a novel integrated approach for precise renal vascular region estimation using advanced segmentation and diagram generation techniques.

## Key findings

- Kidney segmentation Dice score of 95%
- Renal artery centerline overlap ratio of 80%
- Dominant-region estimation Dice coefficient of 80%

## Abstract

This paper presents a new approach for precisely estimating the renal vascular dominant region using a Voronoi diagram. To provide computer-assisted diagnostics for the pre-surgical simulation of partial nephrectomy surgery, we must obtain information on the renal arteries and the renal vascular dominant regions. We propose a fully automatic segmentation method that combines a neural network and tensor-based graph-cut methods to precisely extract the kidney and renal arteries. First, we use a convolutional neural network to localize the kidney regions and extract tiny renal arteries with a tensor-based graph-cut method. Then we generate a Voronoi diagram to estimate the renal vascular dominant regions based on the segmented kidney and renal arteries. The accuracy of kidney segmentation in 27 cases with 8-fold cross validation reached a Dice score of 95%. The accuracy of renal artery segmentation in 8 cases obtained a centerline overlap ratio of 80%. Each partition region corresponds to a renal vascular dominant region. The final dominant-region estimation accuracy achieved a Dice coefficient of 80%. A clinical application showed the potential of our proposed estimation approach in a real clinical surgical environment. Further validation using large-scale database is our future work.