Towards fully automated segmentation of rat cardiac MRI by leveraging deep learning frameworks

  title={Towards fully automated segmentation of rat cardiac MRI by leveraging deep learning frameworks},
  author={Daniel Fern{\'a}ndez-Llaneza and Andrea Gondova and Harris Vince and Arijit Patra and Magdalena Zurek and Peter Konings and Patrik Kagelid and Leif Hultin},
  journal={Scientific Reports},
Automated segmentation of human cardiac magnetic resonance datasets has been steadily improving during recent years. Similar applications would be highly useful to improve and speed up the studies of cardiac function in rodents in the preclinical context. However, the transfer of such segmentation methods to the preclinical research is compounded by the limited number of datasets and lower image resolution. In this paper we present a successful application of deep architectures 3D cardiac… 



Fully Automated Segmentation of the Left Ventricle in Small Animal Cardiac MRI

This paper proposes an efficient and 3D consistent segmentation method for small animal cardiac MR images taking advantage of a combination of long-axis and short-axis images, by combining convolutional neural networks and the guide-point modelling method.

Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

A recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units is proposed.

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.

Deep Learning for Cardiac Image Segmentation: A Review

A review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging, computed tomography, and ultrasound and major anatomical structures of interest.

Automatic segmentation with detection of local segmentation failures in cardiac MRI

The experiments reveal that combining automatic segmentation with manual correction of detected segmentation failures results in improved segmentation and to 10-fold reduction of expert time compared to manual expert segmentation.

Fully Automatic Cardiac Segmentation And Quantification For Pulmonary Hypertension Analysis Using Mice Cine Mr Images

The aim of this work is to develop an automatic tool based on a convolutional neural network for the segmentation of 4 cardiac structures at once in healthy and pathological mice to precisely evaluate biomarkers that may correlate to PH.

A New Framework for Performing Cardiac Strain Analysis from Cine MRI Imaging in Mice

A Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle is developed and the new algorithm can determine the strain differences between normal and diseased hearts.

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

An automated analysis method based on a fully convolutional network achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures.

Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network

A novel deep learning architecture is proposed, named as temporal regression network (TempReg-Net), to accurately identify specific frames from MRI sequences, by integrating the Convolutional Neural Network (CNN) with the Recurrent Neural network (RNN).