Precise estimation of renal vascular dominant regions using spatially aware fully convolutional networks, tensor-cut and Voronoi diagrams

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.

A hybrid approach to full-scale reconstruction of renal arterial network

“…The subtrees created by the initialization process are reminiscent of the vascular dominant regions found in the kidney 38 , 39 . When planning surgical removal of part of the kidney, surgeons identify the first few arterial branches from the renal artery to estimate the subregion supplied by each of the branches 38 , 39 . Each subregion is assumed to only get blood supply from the closest branch.…”

A novel 3D deep learning model to automatically demonstrate renal artery segmentation and its validation in nephron-sparing surgery

“…By manually segmenting kidney shape, vasculature, collecting system, and tumor, Porpiglia proposed a hyperaccuracy 3D model ( 22), which used an augmented reality (AR) technique to guide surgeons during operation (23). Additionally, the goal of purely automatic segmentation of different organs and even renal artery trees using CNNs was achieved (24)(25)(26). We had developed a series of novel CNNs, including 3D_FCN_PPM and DPA-DenseBiasNet, providing a precise segmentation of kidney, tumors, renal arteries and their branches (distal to interlobar arteries) (13,14).…”

New Generation of 3D Virtual Models with Perfusional Zones: Perioperative Assistance for the Best Pedicle Management during Robotic Partial Nephrectomy

“…According to the previous description, Medics Srl © ( , Turin, Italy) developed the 3DVMs for the cases starting from CT scans [ 7 ]. To estimate the perfusion volumes of the organ, the 3DVMs were then implemented by Medics bioengineers with an internal proprietary algorithm, based on the Voronoi diagram, a Euclidean distance-based mathematical method used to determine vascular dominating volumes in other organs, as previously described by Wang et al [ 10 ].…”