Full left ventricle quantification via deep multitask relationships learning

Xue, Wufeng; Brahm, Gary; Pandey, Sachin; Leung, Stephanie; Li, Shuo

doi:10.1016/j.media.2017.09.005

Cited by 138 publications

(113 citation statements)

References 23 publications

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“…The effectiveness of the proposed method was validated under the experimental setting discussed in Section 2.2. We measured the influence of the parameter κ (number of contiguous slices fed to the network) for κ = 1 (single slice), 3, 5 and 7 for the proposed spatio-temporal model based on 3D convolutions, and compare with the state of the art method recently proposed in [10]. Results are presented in Table 1 for a 5-fold cross validation setting (the same experimental setting and dataset was used in [10]).…”

Section: Resultsmentioning

confidence: 99%

“…Note that performance is consistent across folds. Table 1: Sensitivity analysis for the parameter κ (number of neighbouring slices) when using the spatio-temporal model based on 3D convolutions with 5-folds cross validation, compared with the state of the art DMTRL proposed in [10]. Note that incorporating the temporal dynamics by considering multiple slices (κ = 3, 5, 7) makes a significant different with respect the single slice case (κ = 1).…”

Section: Resultsmentioning

confidence: 99%

“…Differently from other methods that rely on more complex mechanisms (like recurrent neural networks [10]) we employ simple 3D convolutions to fuse information coming from temporally contiguous CMR slices. We generated training samples following a circular hypothesis, meaning that first and last slices of the sequences are considered as temporally contiguous.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Left Ventricle Quantification Through Spatio-Temporal CNNs

Debus

Ferrante

2019

Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges

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Cardiovascular diseases are among the leading causes of death globally. Cardiac left ventricle (LV) quantification is known to be one of the most important tasks for the identification and diagnosis of such pathologies. In this paper, we propose a deep learning method that incorporates 3D spatio-temporal convolutions to perform direct left ventricle quantification from cardiac MR sequences. Instead of analysing slices independently, we process stacks of temporally adjacent slices by means of 3D convolutional kernels which fuse the spatio-temporal information, incorporating the temporal dynamics of the heart to the learned model. We show that incorporating such information by means of spatiotemporal convolutions into standard LV quantification architectures improves the accuracy of the predictions when compared with single-slice models, achieving competitive results for all cardiac indices and significantly breaking the state of the art [10] for cardiac phase estimation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Left Ventricle Quantification Through Spatio-Temporal CNNs

Debus

Ferrante

2019

Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges

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“…In [159] the authors detect the area, position and shape of the MI using a model that consists of three layers; first, the heart localization layer is a Fast R-CNN [166] which crops the ROI sequences including the LV; second, the motion statistical layers, which build a time-series architecture to capture the local motion features generated by LSTM-RNN and the global motion features generated by deep optical flows from the ROI sequence; third, the fully connected discriminate layers, which use SAE to further learn the features from the previous layer and a softmax classifier. Xue et al [160] trained an end-to-end deep multitask relationship learning framework on MRI images from 145 subjects with 20 frames each for full LV quantification. It consists of a three layer CNN that extracts cardiac representations, then two parallel LSTM-based RNNs for modeling the temporal dynamics of cardiac sequences.…”

Section: A Magnetic Resonance Imagingmentioning

confidence: 99%

Deep Learning in Cardiology

Bizopoulos

Koutsouris

2019

IEEE Rev. Biomed. Eng.

147

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The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

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“…MRI Heart Lesion classification [76] CADx, computer-aided diagnosis; CADe, computer-aided detection;…”

Section: Image Processing Applications Using Rnn Architecturementioning

confidence: 99%

Prospects of deep learning for medical imaging

2018

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Machine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable problems. Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly. This review article aims to survey deep learning literature in medical imaging and describe its potential for future medical imaging research. First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given. Third, wellknown software tools for deep learning are reviewed. Finally, conclusions with limitations and future directions of deep learning in medical imaging are provided.

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Full left ventricle quantification via deep multitask relationships learning

Cited by 138 publications

References 23 publications

Left Ventricle Quantification Through Spatio-Temporal CNNs

Left Ventricle Quantification Through Spatio-Temporal CNNs

Deep Learning in Cardiology

Prospects of deep learning for medical imaging

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