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

  title={Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?},
  author={Youssef Skandarani and Pierre-Marc Jodoin and Alain Lalande},
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… Expand

Figures and Tables from this paper


Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
How far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies is measured, to open the door to highly accurate and fully automatic analysis of cardiac CMRI. Expand
Learning to Segment Medical Images with Scribble-Supervision Alone
This work investigates training strategies to learn the parameters of a pixel-wise segmentation network from scribble annotations alone and finds that the networks trained on scribbles suffer from a remarkably small degradation in Dice of only 2.9% and 4.5% with respect to a network trained on full annotations. Expand
Cardiac Segmentation With Strong Anatomical Guarantees
This paper presents a framework for producing cardiac image segmentation maps that are guaranteed to respect pre-defined anatomical criteria, while remaining within the inter-expert variability and always anatomically plausible without having to rely on a shape prior. Expand
Fast interactive medical image segmentation with weakly supervised deep learning method
This work proposes a new efficient deep learning method to accurately segment targets from images while generating an annotated dataset for deep learning methods that involves a generative neural network-based prior-knowledge prediction from pseudo-contour landmarks. Expand
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge.
The results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge are presented and the importance of intensity-driven data augmentation is indicated, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Expand
U-Net: Convolutional Networks for Biomedical Image Segmentation
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Expand
Large-scale medical image annotation with crowd-powered algorithms
A multistage segmentation pipeline incorporating a hybrid crowd-algorithm 3-D segmentation algorithm integrated into a medical imaging platform is developed and it is shown that the crowd is able to detect and refine inaccurate organ contours with a quality similar to that of experts. Expand
On the effectiveness of GAN generated cardiac MRIs for segmentation
This work proposes a Variational Autoencoder - Generative Adversarial Networks (GAN) model that can produce highly realistic MRI together with its pixel accurate groundtruth for the application of cine-MR image cardiac segmentation and shows that segmentation with CNNs trained with synthetic annotated images gets competitive results compared to traditional techniques. Expand
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis
The state-of-the-art in handling label noise in deep learning is reviewed and recommendations on methods that can be used to alleviate the effects of different types of label noise on deep models trained for medical image analysis are made. Expand
Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation
DA has emerged as a promising solution to deal with the lack of annotated training data, especially for segmentation tasks and among various DA approaches, domain transformation (DT) and latent feature-space transformation (LFST) are discussed. Expand