Deformable Bayesian Convolutional Networks for Disease-Robust Cardiac MRI Segmentation

2022 ◽  
pp. 296-305
Author(s):  
Mitchell J. Fulton ◽  
Christoffer R. Heckman ◽  
Mark E. Rentschler
Keyword(s):  
Cardiac Mri ◽  
Mri Segmentation ◽  
Convolutional Networks

scholarly journals Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 212
Author(s):  
Youssef Skandarani ◽  
Pierre-Marc Jodoin ◽  
Alain Lalande
Keyword(s):  
Deep Learning ◽  
Cardiac Mri ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Ground Truth ◽  
Cine Mri ◽  
Data Sets ◽  
Mri Segmentation ◽  
Segmentation Evaluation ◽  
Ground Truth Data

Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine–MRI segmentation. Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. The results reveal that generalization performances of a segmentation neural network trained on non-expert ground truth data is, to all practical purposes, as good as that trained on expert ground truth data, particularly when the non-expert receives a decent level of training, highlighting an opportunity for the efficient and cost-effective creation of annotations for cardiac data sets.

RIANet: Recurrent interleaved attention network for cardiac MRI segmentation

2019 ◽  
Vol 109 ◽  
pp. 290-302 ◽  
Author(s):  
Qianqian Tong ◽  
Caizi Li ◽  
Weixin Si ◽  
Xiangyun Liao ◽  
Yaliang Tong ◽  
...  
Keyword(s):  
Cardiac Mri ◽  
Mri Segmentation ◽  
Attention Network
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