Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach

Duan, Jinming; Bello, Ghalib; Schlemper, Jo; Bai, Wenjia; Dawes, Timothy J W; Biffi, Carlo; Marvao, Antonio de; Doumoud, Georgia; O’Regan, Declan P; Rueckert, Daniel

doi:10.1109/tmi.2019.2894322

Cited by 200 publications

(116 citation statements)

References 39 publications

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“…Classification problems, such as pixel-wise classification of CMR images, are also particularly suited to supervised classical ML (79,80) and deep learning approaches (81). These highresolution representations of whole-heart shape and function can encode multiple phenotypes, such as wall thickness or strain, at each of thousands of points in the model (82).…”

Section: Artificial Intelligence In Cardiovascular Imaging-geneticsmentioning

confidence: 99%

Artificial Intelligence for Cardiac Imaging-Genetics Research

Marvao

Dawes

O’Regan

2020

Front. Cardiovasc. Med.

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Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.

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Section: Artificial Intelligence In Cardiovascular Imaging-geneticsmentioning

confidence: 99%

Artificial Intelligence for Cardiac Imaging-Genetics Research

Marvao

Dawes

O’Regan

2020

Front. Cardiovasc. Med.

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“…There are many ways to deal with this issue. The first approach is to assign weights to classes to solve this imbalance problem [19,20]. The second approach is creating synthetic samples from minority class so that majority and minority classes have almost 1:1 ratio.…”

Section: Adasyn Algorithmmentioning

confidence: 99%

T4SS Effector Protein Prediction with Deep Learning

Açıcı

Asuroglu

Erdaş

et al. 2019

Data

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Extensive research has been carried out on bacterial secretion systems, as they can pass effector proteins directly into the cytoplasm of host cells. The correct prediction of type IV protein effectors secreted by T4SS is important, since they are known to play a noteworthy role in various human pathogens. Studies on predicting T4SS effectors involve traditional machine learning algorithms. In this work we included a deep learning architecture, i.e., a Convolutional Neural Network (CNN), to predict IVA and IVB effectors. Three feature extraction methods were utilized to represent each protein as an image and these images fed the CNN as inputs in our proposed framework. Pseudo proteins were generated using ADASYN algorithm to overcome the imbalanced dataset problem. We demonstrated that our framework predicted all IVA effectors correctly. In addition, the sensitivity performance of 94.2% for IVB effector prediction exhibited our framework’s ability to discern the effectors in unidentified proteins.

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“…Khened et al [6] adopted a densely connected CNN model with inception block to segment 2D cardiac MRI. There are also other researchers interesting on the ventricles, myocardium and other tissues MR segmentation [7][8][9][10][11][12][13]. Their methods are mostly based on CNN.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Cardiac Segmentation Framework Based on Multi-sequence MR Image

Liu

Wang

et al. 2020

Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantificati

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LGE CMR is an efficient technology for detecting infarcted myocardium. An efficient and objective ventricle segmentation method in LGE can benefit the location of the infarcted myocardium. In this paper, we proposed an automatic framework for LGE image segmentation. There are just 5 labeled LGE volumes with about 15 slices of each volume. We adopted histogram match, an invariant of rotation registration method, on the other labeled modalities to achieve effective augmentation of the training data. A CNN segmentation model was trained based on the augmented training data by leave-one-out strategy. The predicted result of the model followed a connected component analysis for each class to remain the largest connected component as the final segmentation result. Our model was evaluated by the 2019 Multi-sequence Cardiac MR Segmentation Challenge. The mean testing result of 40 testing volumes on Dice score, Jaccard score, Surface distance, and Hausdorff distance is 0.8087, 0.6976, 2.8727mm, and 15.6387mm, respectively. The experiment result shows a satisfying performance of the proposed framework. Code is available at https://github.com/Suiiyu/MS-CMR2019.

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Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach

Cited by 200 publications

References 39 publications

Artificial Intelligence for Cardiac Imaging-Genetics Research

Artificial Intelligence for Cardiac Imaging-Genetics Research

T4SS Effector Protein Prediction with Deep Learning

An Automatic Cardiac Segmentation Framework Based on Multi-sequence MR Image

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