Cardiovascular Anatomy and Atlas of MR Normal Anatomy

Kastler, B.

doi:10.1007/978-3-540-30702-0_2

Cited by 3 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is done by predicting a 3D label map T also of size N × M × H and whose voxels v = (i, j, k) contain a label T v ∈ {Back, LV, RV, MYO}, where "Back" stands for tissues different than the other three. Following the ACDC structure, X is a series of short axis slices starting from the mitral valve down to the apex [16] (please refer to the ACDC website for more details [17]). In order to enforce a clinically plausible result, a shape prior S is provided which encapsulates the relative position of the LV, RV and MYO.…”

Section: Our Methodsmentioning

confidence: 99%

GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation

Zotti

Luo

Humbert

et al. 2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac centerof-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv "grid" architecture which can be seen as an extension of the U-Net. Experimental results reveal that our method can segment the left and right ventricles as well as the myocardium from a 3D MRI cardiac volume in 0.4 second with an average Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm.

show abstract

Section: Our Methodsmentioning

confidence: 99%

GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation

Zotti

Luo

Humbert

et al. 2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

Anatomical assessment of intrathoracic cardiovascular structures using fast spin-echo double inversion recovery and steady-state free precession magnetic resonance imaging in a normal cat

Arencibia

Corbera

Gil

et al. 2019

Journal of Veterinary Cardiology

View full text Add to dashboard Cite

Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation

Zotti

Luo

Lalande

et al. 2019

IEEE J. Biomed. Health Inform.

173

View full text Add to dashboard Cite

In this paper, we present a novel convolutional neural network (CNN) architecture to segment images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed model is an extension of the U-net that embeds a cardiac shape prior and involves a loss function tailored to the cardiac anatomy. Our system takes as input raw MR images, requires no manual preprocessing or image cropping and is trained to segment the endocardium and epicardium of the left ventricle, the endocardium of the right ventricle, as well as the center of the left ventricle. With its multi-resolution grid architecture, the network learns both high and low-level features useful to register the shape prior as well as accurately localize the borders of the cardiac regions. Experimental results obtained on the ACDC-MICCAI 2017 dataset show that our model segments multi-slices CMRI (left and right ventricle contours) in 0.17 second with an average Dice coefficient of 0.91 and an average 3D Hausdorff distance of 9.5 mm.

show abstract

Cardiovascular Anatomy and Atlas of MR Normal Anatomy

Cited by 3 publications

References 36 publications

GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation

GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation

Anatomical assessment of intrathoracic cardiovascular structures using fast spin-echo double inversion recovery and steady-state free precession magnetic resonance imaging in a normal cat

Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation

Contact Info

Product

Resources

About