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<!DOCTYPE html>
<html>
<head lang="en">
<meta charset="UTF-8">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<title>L-CO-Net</title>
<meta name="description" content="">
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</head>
<body>
<div class="container" id="main">
<div class="row">
<h2 class="col-md-12 text-center" style="padding-bottom:20px">
<b>L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI</br></b>
<span style="font-size:18pt"> EMBC 2020, Oral </span>
<br>
</h2>
</div>
<div class="row">
<div class="col-md-12 text-center">
<ul class="list-inline" style="font-size:18pt">
<li>
<a href="http://ai.stanford.edu/~optas/">
S. M. Kamrul Hasan
</a>
</br>Rochester Institute of Technology
</li>
<li>
<a href="http://aabdelreheem.me">
Cristian A. Linte
</a>
</br>Rochester Institute of Technology
</li>
</ul>
</div>
</div>
<div class="row" style="padding-top:45px">
<div class="col-md-6 col-md-offset-3 text-center">
<ul class="nav nav-pills nav-justified">
<li>
<a href="https://arxiv.org/abs/2004.11253">
<h4><strong>[ Paper ]</strong></h4>
</a>
</li>
<li>
<a href="#video">
<h4><strong>[ Video ]</strong></h4>
</a>
</li>
<li>
<a href="https://github.com/lconet">
<h4><strong>[ Code ]</strong></h4>
</a>
</li>
<li>
<a href="#dataset">
<h4><strong>[ Dataset ]</strong></h4>
</a>
</li>
</ul>
</div>
</div>
<div class="row" style="padding-bottom:30px">
<div class="col-md-8 col-md-offset-2">
<h3>
<b>Abstract</b>
</h3>
<p class="text-justify">
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% (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.
</p>
</div>
</div>
<div class="row" id="video" style="padding-bottom:30px">
<div class="col-md-8 col-md-offset-2">
<h3>
<b>Video</b>
</h3>
<video id="v0" width="100%" loop="" muted="" controls="">-->
<source src="img/hi_res.mp4" type="video/mp4">-->
</video>
</div>
</div>
<div class="row" id="dataset" style="padding-bottom:30px">
<div class="col-md-8 col-md-offset-2">
<h3>
<b>Dataset</b>
</h3>
For this study, we used the ACDC dataset, which is composed of short-axis cardiac cine-MR images acquired from 100 different patients divided into 5 evenly distributed subgroups according to their cardiac condition: normal- NOR, myocardial infarction- MINF, dilated cardiomyopathy- DCM, hypertrophic cardiomyopathyHCM, and abnormal right ventricle- ARV, available as a part of the STACOM 2017 ACDC challenge.</a>.
<br>
</div>
</div>
<div class="row" style="padding-bottom:30px">
<div class="col-md-8 col-md-offset-2">
<h3>
<b>Method: L-CO-Net</b>
</h3>
</div>
<div class="col-md-8 col-md-offset-2">
<figure>
<img src="img/method.png" style="padding-bottom:10px" class="img-responsive" alt="overview">
<figcaption>
</figcaption>
</figure>
</div>
<div class="col-md-8 col-md-offset-2">
Illustration of L-CO-Net framework: (a) ROI detection around LV-RV; (b) Segmentation block consisting of a decoder and an
encoder where each condense block (CB) consists of 3 Layers with a growth rate of k = 16. The transformations within each CB and the
transition-down block are labeled with a cyan and yellow box, respectively.
</div>
</div>
<div class="row" style="padding-bottom:30px">
<div class="col-md-8 col-md-offset-2">
<h3>
<b>Qualitative Results</b>
</h3>
</div>
<div class="col-md-8 col-md-offset-2">
<figure>
<img src="img/listener_qualitative_res.png" style="padding-bottom:10px" class="img-responsive" alt="overview">
<figcaption>
</figcaption>
</figure>
</div>
<div class="col-md-8 col-md-offset-2">
Representative ED and ES frames segmentation results of a complete cardiac cycle from the base (high slice index) to apex (low
slice index) showing RV blood-pool, LV blood-pool, and LV-Myocardium in purple, red, and cyan respectively
</div>
</div>
<div class="row" id="citation" style="padding-bottom:30px">
<div class="col-md-8 col-md-offset-2">
<h3>
<b>Citation</b>
</h3>
If you find our work useful in your research, please consider citing:
<pre class="w3-panel w3-leftbar w3-light-grey">
@inproceedings{hasan2020co,
title={L-CO-Net: Learned Condensation-Optimization Network for Segmentation and Clinical Parameter Estimation from Cardiac Cine MRI},
author={Hasan, SM Kamrul and Linte, Cristian A},
booktitle={2020 42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
pages={1217--1220},
year={2020},
organization={IEEE}
}</pre>
</div>
</div>
<div class="row" id="benchmark" style="padding-bottom:30px">
<div class="col-md-8 col-md-offset-2">
<h3>
<b>L-CO-Net participated post MICCAI STACOM-2017 ACDC Challenge</b>
</h3>
Coming soon!
</div>
</div>
<div class="row" style="padding-bottom:30px">
<div class="col-md-8 col-md-offset-2">
<h3>
<b>Acknowledgements</b>
</h3>
<p class="text-justify">
Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health
under Award No. R35GM128877 and by the Office of Advanced Cyber infrastructure of the National Science Foundation under Award No.
1808530. </a>.
</p>
</div>
</div>
</div>
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</body>
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