Corpus ID: 237571749

Improved AI-based segmentation of apical and basal slices from clinical cine CMR

@article{Harana2021ImprovedAS,
  title={Improved AI-based segmentation of apical and basal slices from clinical cine CMR},
  author={Jorge Mariscal Harana and Naomi Kifle and Reza Razavi and Andrew P. King and Bram Ruijsink and Esther Puyol-Ant{\'o}n},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.09421}
}
Current artificial intelligence (AI) algorithms for short-axis cardiac magnetic resonance (CMR) segmentation achieve human performance for slices situated in the middle of the heart. However, an oftenoverlooked fact is that segmentation of the basal and apical slices is more difficult. During manual analysis, differences in the basal segmentations have been reported as one of the major sources of disagreement in human interobserver variability. In this work, we aim to investigate the… Expand

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References

SHOWING 1-10 OF 15 REFERENCES
A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging
TLDR
The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers, including indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Expand
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge
TLDR
The results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge are presented and the importance of intensity-driven data augmentation is indicated, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Expand
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
TLDR
How far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies is measured, to open the door to highly accurate and fully automatic analysis of cardiac CMRI. Expand
Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function
TLDR
A DL-based framework for automated, quality-controlled characterization of cardiac function from cine CMR, without the need for direct clinician oversight is demonstrated. Expand
Cardiac MRI assessment of right ventricular function in acquired heart disease: factors of variability.
TLDR
Variability of RV functional parameters measurement was strongly influenced by previous observer's experience: it was two to three times superior to that of LV, even for the most experienced observer, and function assessment with cardiac MRI in AHD patients is much more variable and time-consuming. Expand
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.
Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable imageExpand
ImageNet classification with deep convolutional neural networks
TLDR
A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective. Expand
Densely Connected Convolutional Networks
TLDR
The Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion, and has several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. Expand
MobileNetV2: Inverted Residuals and Linear Bottlenecks
TLDR
A new mobile architecture, MobileNetV2, is described that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes and allows decoupling of the input/output domains from the expressiveness of the transformation. Expand
Very Deep Convolutional Networks for Large-Scale Image Recognition
TLDR
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. Expand
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