Paper Info
Title
L-Co-Net: Learned Condensation-Optimization Network For Segmentation And Clinical Parameter Estimation From Cardiac Cine Mri
Abstract
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 patient groups imaged throughout the cardiac cycle. Our technique achieved Dice scores of 96.8% (LV blood-pool), 93.3% (RV blood-pool), and 90.0% (LV Myocardium) with five-fold 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.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9176491
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
Keywords
DocType
Volume
Cine MRI, learned group-convolution, condensation-optimization network, ventricle segmentation
Conference
2020
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
S M Kamrul Hasan100.34
Cristian A. Linte29324.09