L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI

04/21/2020
by   S. M. Kamrul Hasan, et al.
0

In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation. We validated our framework on the ACDC dataset featuring one healthy and four pathology groups imaged throughout the cardiac cycle. Our technique achieved Dice scores of 96.8 blood-pool), 93.3 cross-validation and yielded similar clinical parameters as those estimated from the ground truth segmentation data. Based on these results, this technique has the potential to become an efficient and competitive cardiac image segmentation tool that may be used for cardiac computer-aided diagnosis, planning, and guidance applications.

READ FULL TEXT

page 2

page 3

page 5

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
01/16/2018

Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers

Deep fully convolutional neural network (FCN) based architectures have s...
research
11/03/2016

Integrating Atlas and Graph Cut Methods for LV Segmentation from Cardiac Cine MRI

Magnetic Resonance Imaging (MRI) has evolved as a clinical standard-of-c...
research
04/12/2021

Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation

Deep learning methods have reached state-of-the-art performance in cardi...
research
08/30/2022

On the Automated Segmentation of Epicardial and Mediastinal Cardiac Adipose Tissues Using Classification Algorithms

The quantification of fat depots on the surroundings of the heart is an ...
research
05/03/2023

Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?

Deep learning-based methods have spearheaded the automatic analysis of e...
research
08/31/2018

Automated segmentation on the entire cardiac cycle using a deep learning work-flow

The segmentation of the left ventricle (LV) from CINE MRI images is esse...

Please sign up or login with your details

Forgot password? Click here to reset