Automated Kidney Segmentation in Magnetic Resonance Imaging using U-Net
Year: 2019
Type: dissertation
Abstract: Manual analysis of medical images such as magnetic resonance imaging (MRI) requires a trained professional, is time-consuming and results may vary between experts. We propose an automated method for kidney segmentation using a convolutional Neural Network (CNN) model based on the U-Net architecture. Investigations are done to compare segmentations between trained experts, inexperienced operators and the Neural Network model, showing near human expert level performance from the Neural Network. Stratified sampling is performed when selecting which subject volumes to perform manual segmentations on to create training data. Experiments are run to test the effectiveness of transfer learning and data augmentation and we show that one of the most important components of a successful machine learning pipeline is larger quantities of carefully annotated data for training.
Author: Andreas Östling
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2970528632