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EJECTION FRACTION CLASSIFICATION INTRANSTHORACIC ECHOCARDIOGRAPHYUSING A DEEP LEARNING APPROACH
2018 IEEE 31st International Symposium on Computer-Based Medical Systems 18/06/2018
João Figueira Silva – joaofsilva@ua.pt
AGENDAIntroduction
Related Work
Methods
Results
Discussion
Conclusion
EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH 2
INTRODUCTION:
3
Cardiovascular Diseases (CVDs) are the leading cause of death worldwide
Accounted for approximately 17.7 million deaths in 2015
Highly correlated with left ventricle (LV) function indices:
Ejection Fraction (EF)
Wall thicknessVentricular mass
…
EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
INTRODUCTION:
4
Techniques to assess patient condition:
Computed Tomography (CT)
Magnetic Resonance Imaging (MRI)
Transthoracic Echocardiography (TTE)
TTE is widely available, cheaper, and portable
Considered a first line of diagnosis technique
EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
5
PhysicianTTE Exam
Metrics3D-CNN
Manual Segmentation Metrics
EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
INTRODUCTION:
RELATED WORK:
6
Automatic left ventricle segmentation:Active ContoursSupervised Learning Methods
Issue: require prior knowledge on the left ventricle shape
Deep learning methods using 2D echocardiographyIssue: requires prior detection of the systole and diastole frames by the user
EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
RELATED WORK:
7
3D information has already been used in EF estimation with data from:Magnetic Resonance Imaging3D Echocardiography
Issue: both techniques have higher associated costs than 2D Echocardiography
OBJECTIVE: Estimate ejection fraction using TTE cineloops (2D echo), consideringtime as the 3rd dimension
EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
METHODS: DATASET
8
Exams containing TTE cineloops, annotated information and free text report
Manual selection of the Apical 4 Chambers view, EF data extraction from meta-data
Extraction of 30 sequential frames from each cineloop
Automatic extraction of the region of interest to mask burned in PHI
Frame size: 128 x 128 pixels
Categorization of EF values
EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
METHODS: DATASET
9EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
10EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
METHODS: DATASET
METHODS: 3D-CNN
11EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
Input Bottom Layers
3D Convolution
3D Global Avg Pool
Batch Normalization
Leaky ReLU
Top Layers
<45%
45%-55%
55%-75%
>75%
ClassificationMiddle Layers
Residual Block Y
Residual Block Z
Residual Block X
+
ResidualBlock
Convblock X/Y/Z
Batch NormalizationLeaky ReLU
Batch Normalization
Convblock X/Y/ZLeaky ReLU
+
3D Conv 3F (1x1x1)
3D Conv 6F (1x1x1)
3D Conv 3F Z(1x3x1) X(1x1x3)
Y(3x1x1)
Convblock X/Y/Z
3D Convolution
3D Global Avg Pool
Batch NormalizationLeaky ReLU
Fully -Connected Dropout
3D for temporal knowledge
Asymmetric filters
Residual blocks
Batch normalization and Dropout
Mini-batch: 64 exams
Random grid search
12
RESULTS
Accuracy: 78%
F1 score (one vs all)
EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
13
DISCUSSION
EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
Class imbalance is reflected on model performance
Class 2 is a transitional class: contains both healthy and unhealthy exams
Discrepancy between values in meta-data and in clinical text reports
Noise in TTE images: possible incorrect data representation
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CVDs are the leading cause of death worldwide
First line of diagnosis often based on TTE
Metrics like EF frequently require manual annotations
Novel 3D-CNN proposed to automate the process, obtaining promising results
Possible improvements:
Automatic selection of Apical 4 chambers view in TTE exams to expand dataset
Key-point selection in TTE cineloops
CONCLUSION
EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHY USING A DEEP LEARNING APPROACH
THANK YOU FORYOUR ATTENTION
João Figueira Silva - joaofsilva@ua.pt 18/06/2018
EJECTION FRACTION CLASSIFICATION IN TRANSTHORACIC ECHOCARDIOGRAPHYUSING A DEEP LEARNING APPROACH
João Figueira Silva, Jorge Miguel Silva, António Guerra, Sérgio Matos and Carlos Costa2018 IEEE 31st International Symposium on Computer-Based Medical Systems
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