Discriminative dictionary learning for local LV wall motion classification in cardiac MRI

Mantilla, José Iván Alfonso; Paredes, José Luís; Bellanger, Jean-Jacques; Donal, Erwan; Leclercq, Christophe; Garreau, Mireille

doi:10.1016/j.eswa.2019.04.010

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…In recent years, Cardiac Magnetic Resonance (CMR) has become a crucial imaging modality in clinical cardiology practice due to its high signal-to-noise ratio, noninvasive imaging to cardiac chambers, no need for geometric assumptions, and great vessels [1,2]. Although much effort has been devoted to left ventricle quantification over the last several decades [2][3][4][5][6][7][8], it remains in the research stage and the reported algorithms are still not robust and flexible enough to support clinic practice due to the complexity of medical imaging. erefore, left ventricle quantification is still acknowledged as a challenge with much room for improvements in robustness, flexibility, and accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐Indices Quantification for Left Ventricle via DenseNet and GRU‐Based Encoder‐Decoder with Attention

Zhang

et al. 2021

Complexity

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More and more research on left ventricle quantification skips segmentation due to its requirement of large amounts of pixel-by-pixel labels. In this study, a framework is developed to directly quantify left ventricle multiple indices without the process of segmentation. At first, DenseNet is utilized to extract spatial features for each cardiac frame. Then, in order to take advantage of the time sequence information, the temporal feature for consecutive frames is encoded using gated recurrent unit (GRU). After that, the attention mechanism is integrated into the decoder to effectively establish the mappings between the input sequence and corresponding output sequence. Simultaneously, a regression layer with the same decoder output is used to predict multi-indices of the left ventricle. Different weights are set for different types of indices based on experience, and l2-norm is used to avoid model overfitting. Compared with the state-of-the-art (SOTA), our method can not only produce more competitive results but also be more flexible. This is because the prediction results in our study can be obtained for each frame online while the SOTA only can output results after all frames are analyzed.

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Section: Introductionmentioning

confidence: 99%

“…Furthermore, segmentation-based methods usually introduce two phases to quantify the left ventricle, resulting in difficulty to optimize the framework as a whole. At present, the end-to-end framework is increasingly popular with the development of deep learning technology [3,10].…”

Section: Introductionmentioning

confidence: 99%

Multi‐Indices Quantification for Left Ventricle via DenseNet and GRU‐Based Encoder‐Decoder with Attention

Zhang

et al. 2021

Complexity

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FDSR: A new fuzzy discriminative sparse representation method for medical image classification

Ghasemi

Kelarestaghi

Eshghi

et al. 2020

Artificial Intelligence in Medicine

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A systematic review of multi-slice and multi-frame descriptors in cardiac MRI exams

Delmondes

Nunes

2022

Computer Methods and Programs in Biomedicine

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Discriminative dictionary learning for local LV wall motion classification in cardiac MRI

Cited by 6 publications

References 36 publications

Multi‐Indices Quantification for Left Ventricle via DenseNet and GRU‐Based Encoder‐Decoder with Attention

Multi‐Indices Quantification for Left Ventricle via DenseNet and GRU‐Based Encoder‐Decoder with Attention

FDSR: A new fuzzy discriminative sparse representation method for medical image classification

A systematic review of multi-slice and multi-frame descriptors in cardiac MRI exams

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