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

arXiv: https://arxiv.org/abs/1908.01543 · HTML: https://mlsanity.com/paper/1908.01543/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [A Hybrid Approach to Full-Scale Reconstruction of Renal Arterial Network](https://mlsanity.com/paper/2303.01837/) (2023)

## Abstract (arXiv)

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.