# Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1712.07022 · HTML: https://mlsanity.com/paper/1712.07022/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework](https://mlsanity.com/paper/2505.07573/) (2025)
- [Kidney abnormality segmentation in thorax-abdomen CT scans](https://mlsanity.com/paper/2309.03383/) (2023)
- [Can a single image processing algorithm work equally well across all phases of DCE-MRI?](https://mlsanity.com/paper/2306.12988/) (2023)

## Abstract (arXiv)

Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal information at the same time in order to learn the tasks of localization and segmentation of kidneys, respectively. Segmentation performance is evaluated on both normal and abnormal kidneys with varying levels of hydronephrosis. We achieved a mean dice coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric patients, respectively.