Extreme Cardiac MRI Analysis Challenge under Respiratory Motion
Creators: name: Wang, Shuo
name: Xinrong Chen
name: Chengyan Wang
name: Xutong Kuang
name: Wenjia Bai
name: Chen, Chen
name: Chengliang Dai
name: Qin, Chen
name: Yuanhan Mo
name: Wang, He
name: Shi, Zhang
name: Chenchen Dai
Publisher: name: Zenodo
Year: 2022
Subjects: cardiac MRI
model robustness
quality control
respiratory motion
MICCAI
Challenge

description: Cardiac magnetic resonance imaging (CMR) is the current gold-standard modality for the evaluation of cardiac structure and function [1]. Machine learning-based approaches have achieved remarkable performance in previous CMR challenges (e.g., ACDC [2], M&amp;Ms [3]). However, in clinical practice, the model performance is challenged by inconsistent imaging environments (e.g., vendors and protocols), population shifts (normal v.s. pathological cases) and unexpected human behaviours (e.g., body movements). It is useful to investigate potential failure modes by exposing a trained machine learning model to extreme cases in a ‘stress test’. To date, existing challenges on model generalisability focus on the vendor variability and anatomical structure variations while the implications of human behaviours are less explored. For CMR acquisition, respiration motion is one of the major problems. Patients with acute symptoms cannot adhere to the breath-hold instructions, resulting in degraded image quality and inaccurate analysis. <br> In this challenge, we introduce ‘CMRxMotion’, a realistic cardiac MRI dataset including extreme cases with different levels of respiratory motions. We actively attack the standard of procedure (SOP) by redesigning the participant’s behaviours during acquisition. For 60 healthy volunteers, we perform clinical CMR scans using the same MRI scanner. Volunteers are trained to act in 4 manners, respectively: a) adhere to the breath-hold instructions; b) halve the breath-hold period; c) breathe freely; and d) breathe intensively. Therefore, for a single volunteer, we obtain a set of CMR images under 4 levels of respiratory motion. Radiologists first assess the image quality and recognise the images with non-diagnostic quality. For those images with diagnostic quality,<br> radiologists further segment the left ventricle, left ventricle myocardium and right ventricle. We will release the images, quality scores and ground-truth segmentations (if any) of 120 cases from 30 volunteers for training and validation. The remaining 30 volunteers (120 cases) will be withheld for the test. <br> Based on this extreme cardiac dataset, we design two tasks: 1) the image quality control task and 2) the segmentation task. For Task 1, challenge participants are expected to develop a quality control model to recognise the images with non-diagnostic quality. For Task 2, participants are expected to develop segmentation models robust to respiratory motion artefacts. We aim to provide such an extreme CMR dataset that allows for a more comprehensive evaluation of model robustness under implications of respiratory motion. <br> [1] Schulz-Menger, Jeanette, et al. "Standardized image interpretation and post-processing in cardiovascular magnetic resonance-2020 update." Journal of Cardiovascular Magnetic Resonance 22.1 (2020): 1-22.<br> [2] Bernard, Olivier, et al. "Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?." IEEE transactions on medical imaging 37.11 (2018): 2514-2525.<br> [3] Campello, Víctor M., et al. "Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&amp;Ms challenge." IEEE Transactions on Medical Imaging 40.12 (2021): 3543-3554.