Deep learning kidney segmentation with very limited training data using a cascaded convolution neural network.

ABSTRACT:

Background

Deep learning segmentation requires large datasets with ground truth. Image annotation is time consuming and leads to shortages of ground truth data for clinical imaging. This study is to investigate the feasibility of kidney segmentation using deep learning convolution neural network (CNN) models trained with MR images from only a few subjects.

Methods

A total of 60 subjects from two cohorts were included in this study. The first cohort of 20 subjects from publicly available data was used for training and testing. The second cohort of 40 subjects with renal masses from our institution was used for testing only. A few-shot deep learning approach using 3D augmentation was investigated. T1-weighted images in the first cohort were used for training and testing. Casca

SUBMITTER: Guo J 

PROVIDER: S-EPMC9084530 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature