Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes

TL;DR

Kid-Net is a convolutional neural network designed for fast, high-resolution segmentation of kidney vessels from CT volumes, addressing data imbalance and reducing manual annotation time.

Contribution

The paper introduces a novel training schema with dynamic weighting, random sampling, and 3D patch segmentation for improved kidney vessel segmentation.

Findings

Segmentation of a 512x512x512 CT volume takes 1-2 minutes.

Kid-Net reduces manual annotation time from hours to minutes.

Quantitative and qualitative results demonstrate high accuracy.

Abstract

Semantic image segmentation plays an important role in modeling patient-specific anatomy. We propose a convolution neural network, called Kid-Net, along with a training schema to segment kidney vessels: artery, vein and collecting system. Such segmentation is vital during the surgical planning phase in which medical decisions are made before surgical incision. Our main contribution is developing a training schema that handles unbalanced data, reduces false positives and enables high-resolution segmentation with a limited memory budget. These objectives are attained using dynamic weighting, random sampling and 3D patch segmentation. Manual medical image annotation is both time-consuming and expensive. Kid-Net reduces kidney vessels segmentation time from matter of hours to minutes. It is trained end-to-end using 3D patches from volumetric CT-images. A complete segmentation for a 512x512x512 CT-volume is obtained within a few minutes (1-2 mins) by stitching the output 3D patches together. Feature down-sampling and up-sampling are utilized to achieve higher classification and localization accuracies. Quantitative and qualitative evaluation results on a challenging testing dataset show Kid-Net competence.