Automatic Cardiac Segmentation Using Semantic Information from Random Forests

Mahapatra, Dwarikanath

doi:10.1007/s10278-014-9705-0

Cited by 22 publications

(13 citation statements)

References 44 publications

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“…They showed notable accuracy and processing time improvement over other methods while deformed RV shape and patient movement during the scan are the limitations of their method . Current learning‐based approaches, such as probabilistic boosting trees and random forests, depend on the quality and extent of annotated training data and are computationally expensive .…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of the right ventricle from cardiac MRI using a learning‐based approach

Avendi

Kheradvar

Jafarkhani

2017

Magnetic Resonance in Med

122

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Purpose: This study aims to accurately segment the right ventricle (RV) from cardiac MRI using a fully automatic learningbased method. Methods: The proposed method uses deep learning algorithms, i.e., convolutional neural networks and stacked autoencoders, for automatic detection and initial segmentation of the RV chamber. The initial segmentation is then combined with the deformable models to improve the accuracy and robustness of the process. We trained our algorithm using 16 cardiac MRI datasets of the MICCAI 2012 RV Segmentation Challenge database and validated our technique using the rest of the dataset (32 subjects). Results: An average Dice metric of 82.5% along with an average Hausdorff distance of 7.85 mm were achieved for all the studied subjects. Furthermore, a high correlation and level of agreement with the ground truth contours for end-diastolic volume (0.98), end-systolic volume (0.99), and ejection fraction (0.93) were observed. Conclusion: Our results show that deep learning algorithms can be effectively used for automatic segmentation of the RV. Computed quantitative metrics of our method outperformed that of the existing techniques participated in the MICCAI 2012 challenge, as reported by the challenge organizers.

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Section: Introductionmentioning

confidence: 99%

Automatic segmentation of the right ventricle from cardiac MRI using a learning‐based approach

Avendi

Kheradvar

Jafarkhani

2017

Magnetic Resonance in Med

122

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“…RF [23] is a common method for ensemble learning whose training algorithm relies on bagging integration and random attribute selection in the construction of the decision tree. The training of one RF is shown in a blue arrow line in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

“…RF is a committee of weak learners (e.g., decision tree) to solve classification and regression problems without manually specifying some features through the construction and combination of multiple decision trees and random selection of attributes [19, 20], which can be used to cope with the complex structural characteristics of biological images. RF has been widely explored from medical image-processing fields, especially detection tasks, including early identification or prediction of Alzheimer's disease [21], adrenal gland abnormality detection [22], and automatic cardiac segmentation [23].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Pathological Glomerular Basement Membrane in Transmission Electron Microscopy Images with Random Forest Stacks

Cao

et al. 2019

Computational and Mathematical Methods in Medicine

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Pathological classification through transmission electron microscopy (TEM) is essential for the diagnosis of certain nephropathy, and the changes of thickness in glomerular basement membrane (GBM) and presence of immune complex deposits in GBM are often used as diagnostic criteria. The automatic segmentation of the GBM on TEM images by computerized technology can provide clinicians with clear information about glomerular ultrastructural lesions. The GBM region on the TEM image is not only complicated and changeable in shape but also has a low contrast and wide distribution of grayscale. Consequently, extracting image features and obtaining excellent segmentation results are difficult. To address this problem, we introduce a random forest- (RF-) based machine learning method, namely, RF stacks (RFS), to realize automatic segmentation. Specifically, this work proposes a two-level integrated RFS that is more complicated than a one-level integrated RF to improve accuracy and generalization performance. The integrated strategies include training integration and testing integration. Training integration can derive a full-view RFS1 by simultaneously sampling several images of different grayscale ranges in the train phase. Testing integration can derive a zoom-view RFS2 by separately sampling the images of different grayscale ranges and integrating the results in the test phase. Experimental results illustrate that the proposed RFS can be used to automatically segment different morphologies and gray-level basement membranes. Future study on GBM thickness measurement and deposit identification will be based on this work.

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“…A landmark detection challenge in 2012 made 200 cases with manual ground truth available for validation and benchmarking [42]. Machine learning methods show promise for landmark detection but require large datasets with manual ground truth in order to train the algorithms [47, 48]. …”

Section: Challengesmentioning

confidence: 99%

Challenges of Cardiac Image Analysis in Large-Scale Population-Based Studies

et al. 2015

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Large-scale population-based imaging studies of preclinical and clinical heart disease are becoming possible due to the advent of standardized robust non-invasive imaging methods and infrastructure for big data analysis. This gives an exciting opportunity to gain new information about the development and progression of heart disease across population groups. However, the large amount of image data and prohibitive time required for image analysis present challenges for obtaining useful derived data from the images. Automated analysis tools for cardiac image analysis are only now becoming available. This paper reviews the challenges and possible solutions to the analysis of big imaging data in population studies. We also highlight the potential of recent large epidemiological studies using cardiac imaging to discover new knowledge on heart health and well-being.

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Automatic Cardiac Segmentation Using Semantic Information from Random Forests

Cited by 22 publications

References 44 publications

Automatic segmentation of the right ventricle from cardiac MRI using a learning‐based approach

Automatic segmentation of the right ventricle from cardiac MRI using a learning‐based approach

Automatic Segmentation of Pathological Glomerular Basement Membrane in Transmission Electron Microscopy Images with Random Forest Stacks

Challenges of Cardiac Image Analysis in Large-Scale Population-Based Studies

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