Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI

11/15/2020
by   Peter M. Full, et al.
0

Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart related diseases. Recently, deep neural networks have demonstrated successful automatic segmentation, thus alleviating the burden of time-consuming manual contouring of cardiac structures. Moreover, frameworks such as nnU-Net provide entirely automatic model configuration to unseen datasets enabling out-of-the-box application even by non-experts. However, current studies commonly neglect the clinically realistic scenario, in which a trained network is applied to data from a different domain such as deviating scanners or imaging protocols. This potentially leads to unexpected performance drops of deep learning models in real life applications. In this work, we systematically study challenges and opportunities of domain transfer across images from multiple clinical centres and scanner vendors. In order to maintain out-of-the-box usability, we build upon a fixed U-Net architecture configured by the nnU-net framework to investigate various data augmentation techniques and batch normalization layers as an easy-to-customize pipeline component and provide general guidelines on how to improve domain generalizability abilities in existing deep learning methods. Our proposed method ranked first at the Multi-Centre, Multi-Vendor Multi-Disease Cardiac Image Segmentation Challenge (M Ms).

READ FULL TEXT
research
09/17/2019

Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning

The goal of this project is to use magnetic resonance imaging (MRI) data...
research
08/26/2020

Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation

Cine cardiac magnetic resonance (CMR) has become the gold standard for t...
research
04/05/2020

CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation

With the advent of Cardiac Cine Magnetic Resonance (CMR) Imaging, there ...
research
08/03/2020

Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation

Tackling domain shifts in multi-centre and multi-vendor data sets remain...
research
12/21/2022

TMS-Net: A Segmentation Network Coupled With A Run-time Quality Control Method For Robust Cardiac Image Segmentation

Recently, deep networks have shown impressive performance for the segmen...
research
10/28/2021

Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis

The shape and motion of the heart provide essential clues to understandi...

Please sign up or login with your details

Forgot password? Click here to reset