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<!DOCTYPE html>
<!--[if lt IE 9 ]><html class="no-js oldie" lang="en"> <![endif]-->
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<title>DeepMedic</title>
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<h3 data-aos="fade-up">DeepMedic</h3>
<h1 data-aos="fade-up">
<!--A multi-scale 3D Deep Convolutional Neural Network tool for 3D image segmentation.-->
Deep Learning Suite for 3D Image Segmentation
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<h1 class="intro-header" data-aos="fade-up">About DeepMedic:</h1>
</div>
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<p class="lead" data-aos="fade-up">
DeepMedic is software for 3D image segmention, based on
a <a href="https://www.sciencedirect.com/science/article/pii/S1361841516301839/">multi-scale
3D Deep Convolutional Neural Network</a>,
from the <a href="https://biomedia.doc.ic.ac.uk/">BioMedIA Group</a> of Imperial College London.
The system has been shown to achieve excellent performance on brain lesion segmentation
for various tasks, including brain injuries, brain tumors, and
ischemic stroke lesions.
This method is part of the systems that won
the <a href="http://www.isles-challenge.org/">ISLES 2015</a>
and <a href="https://www.med.upenn.edu/sbia/brats2017/rankings.html/">BRATS 2017</a> competitions.
</p>
</div>
</div>
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<h1 class="intro-header" data-aos="fade-up">What we are making:</h1>
</div>
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<div class="row about-features">
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<div class="service-content">
<h3>3D Deep Learning Suite</h3>
<p>A complete set of versatile and easy to use tools to train, evaluate,
and make use of 3D image segmentation models in a simple and intuitive manner for non-technical users.</p>
</div>
</div> <!-- /bgrid -->
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<h3>Tried and Tested Performance</h3>
<p>DeepMedic is the winning algorithm in <a href="http://www.isles-challenge.org/">ISLES 2015</a> competition,
part of the winning system in <a href="https://arxiv.org/pdf/1811.02629.pdf">BraTS 2017</a>, and
widely used for variety of segmentation tasks in 3D medical imaging.</p>
</div>
</div> <!-- /bgrid -->
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<h3>Integrated Image Pre-Processor</h3>
<p>The framework includes automated data harmonisation,
and informative dataset metadata checks, made easily and intuitively customisable
via its Graphical User Interface.</p>
</div>
</div> <!-- /bgrid -->
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<h3>Graphical Interface</h3>
<p>Graphical User Interface enables easy training and testing of
Deep Learning models.</p>
</div>
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<h3>Easy To Configure And Customize</h3>
<p>Easy to customize and tune for your data and needs to achieve higher performance levels.</p>
</div>
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<h3>Pre-Trained Model Zoo</h3>
<p>Distributing pre-trained segmentation models for common medical tasks.</p>
</div>
</div> <!-- /bgrid -->
</div> <!-- end features-list -->
</div> <!-- end about-features -->
</section> <!-- end about -->
<!-- Testimonials Section
================================================== -->
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<h1 class="intro-header" data-aos="fade-up">Testimonials:</h1>
</div>
</div>
<div class="row owl-wrap">
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<div class="slides owl-carousel">
<div>
<p>
DeepMedic was successfully adopted in the clinical workflow
[for segmenting organs-at-risk in prostate radiotherapy] maintaining in the
clinical setting the accuracy obtained in the feasibility study.
</p>
<div class="testimonial-author">
<img src="images/umc_utrecht.svg" alt="Author image">
<div class="author-info">
<a href="https://ro-journal.biomedcentral.com/articles/10.1186/s13014-020-01528-0"
target="_blank">
Savenije et al., Radiation Oncology, 2020.
</a>
<span>
UMC Utrecht
</span>
</div>
</div>
</div>
<div>
<p>
no statistically significant differences were found between the performance metrics
[between DeepMedic and human reader for stroke lesion segmentation in DW MRI]
</p>
<div class="testimonial-author">
<img src="images/mgh_harvard_clean.png" alt="Author image">
<div class="author-info">
<a href="https://www.ahajournals.org/doi/full/10.1161/STROKEAHA.119.025373"
target="_blank">
Wu et al., Stroke, 2019
</a>
<span>
Massachusetts General Hospital / Harvard University
</span>
</div>
</div>
</div>
<div>
<p>
the system [DeepMedic] learned to outperform its “human teachers” and
produced output that was better, on average, than its training data.
</p>
<div class="testimonial-author">
<img src="images/Mayo-Clinic-Logo2.png" alt="Author image">
<div class="author-info">
<a href="https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-7/issue-5/055501/Deep-neural-network-to-locate-and-segment-brain-tumors-outperformed/10.1117/1.JMI.7.5.055501.full"
target="_blank">
Mitchell et al., Journal of Medical Imaging, 2020
</a>
<span>
Mayo Clinic
</span>
</div>
</div>
</div>
<div>
<p>
the Dice score of the algorithm results were comparable with the Dice scores between human
readers in our subcohort [for stroke lesion segmentation in DW MRI]
</p>
<div class="testimonial-author">
<img src="images/mgh_harvard_cambr_clean.png" alt="Author image">
<div class="author-info">
<a href="http://www.ajnr.org/content/early/2019/05/30/ajnr.A6077"
target="_blank">
Winzeck et al., American Journal of Neuroradiology, 2019
</a>
<span>
MGH, Harvard University, Cambridge University
</span>
</div>
</div>
</div>
<div>
<p>
a whole training process within a single day utilizing a NVIDIA GeForce GTX 580 graphics
board which makes this library also very interesting for research purposes on low-end GPU hardware.
</p>
<div class="testimonial-author">
<img src="images/mevis.svg" alt="Author image">
<div class="author-info">
<a href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10575/105752F/Exploring-DeepMedic-for-the-purpose-of-segmenting-white-matter-hyperintensity/10.1117/12.2292809.short"
target="_blank">
Lippert et al. SPIE 2018
</a>
<span>
Fraunhofer MEVIS
</span>
</div>
</div>
</div>
<div>
<div class="testimonial-no-quote">
<p>
Various independent studies found that DeepMedic out-performed
other state-of-the-art deep-learning models in different tasks,
such as for liver segmentation in CT or brain vessel segmentation from cerebrovascular images.
</p>
</div>
<div class="testimonial-author">
<div class="row"
style="
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<div class="col-md-6">
<div class="img-div">
<img src="images/deu.png" alt="Author image">
</div>
<div class="author-info">
<a href="https://arxiv.org/abs/2001.09647"
target="_blank">
Kavur et al. arxiv 2020
</a>
<span>
Dokuz Eylul University
</span>
</div>
</div>
<div class="col-md-6">
<div class="img-div">
<img src="images/ub.png" alt="Author image">
</div>
<div class="author-info">
<a href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11314/113142W/Multi-resolution-CNN-for-brain-vessel-segmentation-from-cerebrovascular-images/10.1117/12.2549761.full"
target="_blank">
Patel et al. SPIE 2020
</a>
<span>
University at Buffalo
</span>
</div>
</div>
</div>
</div>
</div>
</div> <!-- end slides -->
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<!-- download
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<h1 class="intro-header" data-aos="fade-up">Download DeepMedic.</h1>
<p class="lead" data-aos="fade-up">
DeepMedic is hosted on GitHub, where the codebase is kept, maintained, and updated.
</p>
<ul class="download-badges">
<li><a href="https://github.com/deepmedic/deepmedic" title="" class="badge-github"
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<p>
The DeepMedic software is published under a BSD 3-Clause License.
A copy of this license is present in the root directory of the GitHub repository.
</p>
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<h4>Contact</h4>
<p>
Biomedical Image Analysis Group,<br>
Department of Computing, <br>
Imperial College London, <br>
London SW7 2AZ, UK
</p>
<p>
konstantinos.kamnitsas12@ic.ac.uk <br>
</p>
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