![Quality Control
Introduction – Proposal
• Novel deep generative mode-based framework for quality control of cardiac MRI
segmentation
• First learns a manifold of good-quality image-segmentation pairs using a generative
model.
• The quality of a given test segmentation is then assessed by evaluating the difference
from its projection onto the good-quality manifold.
Introduction / Related Work / Methods and Experiments / Conclusion
02
[Overview of proposed framework]](https://image.slidesharecdn.com/cmrqualitycontrol-201025124348/85/Deep-Generative-model-based-quality-control-for-cardiac-MRI-segmentation-4-320.jpg)
![Related Work
Introduction / Related Work / Methods and Experiments / Conclusion
04
Learning-based quality control
[1] Kohlberger, T., Singh, V., Alvino, C., Bahlmann, C., Grady, L.: Evaluating segmentation error without ground truth. In: International Conference on Medical Image Computing
and Computer-Assisted Intervention, Springer (2012) 528–536
[2] Robinson, R., Oktay, O., Bai, W., Valindria, V.V., Sanghvi, M.M., Aung, N., Paiva, J.M., Zemrak, F., Fung, K., Lukaschuk, E., et al.: Real-time prediction of segmentation quality. In:
International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2018) 578–585
[3] Liu, F., Xia, Y., Yang, D., Yuille, A.L., Xu, D.: An alarm system for segmentation algorithm based on shape model. In: Proceedings of the IEEE International Conference on
Computer Vision. (2019) 10652–10661
• [1] Proposed 42 hand-crafted features
based on intensity and appearance and
achieved an accuracy of 85% in
detecting segmentation failure.
• [2] developed a CNN-based method for
real-time regression of the Dice
similarity metric from image-
segmentation pairs.
• [3] used a variational auto-encoder
(VAE) for learning the shape features of
segmentation in an unsupervised
manner.](https://image.slidesharecdn.com/cmrqualitycontrol-201025124348/85/Deep-Generative-model-based-quality-control-for-cardiac-MRI-segmentation-6-320.jpg)
![Related Work
Introduction / Related Work / Methods and Experiments / Conclusion
05
Registration-based quality control
[4] Valindria, V.V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E.O., Rockall, A.G., Rueckert, D., Glocker, B.: Reverse classification accuracy: predicting segmentation performance
in the absence of ground truth. IEEE transactions on medical imaging 36(8) (2017) 1597–1606
• [4] proposed the concept of reverse
classification accuracy (RCA) to predict
segmentation quality and achieved
good performance on a large-scale
cardiac MRI dataset.](https://image.slidesharecdn.com/cmrqualitycontrol-201025124348/85/Deep-Generative-model-based-quality-control-for-cardiac-MRI-segmentation-7-320.jpg)
Deep Generative model-based quality control for cardiac MRI segmentation
- 1. Deep Generative Model-based Quality Control for Cardiac MRI Segmentation Hwang seung hyun Yonsei University Severance Hospital CCIDS BioMedIA, Imperial College London, UK| MICCAI 2020 2020.09.27
- 2. Introduction Related Work Methods and Experiments 01 02 03 Conclusion 04 Yonsei Unversity Severance Hospital CCIDS Contents
- 3. Quality Control Introduction – Background • When a trained segmentation model is deployed into the real clinical world, the model may not perform optimally. - Degraded Image Quality - Domain Shift Issues • Need to develop an Automated quality control method that can detect poor segmentations and feedback to clinicians. • Reliable quality control (QC) of cardiac MRI segmentation is highly desired. Introduction / Related Work / Methods and Experiments / Conclusion 01
- 4. Quality Control Introduction – Proposal • Novel deep generative mode-based framework for quality control of cardiac MRI segmentation • First learns a manifold of good-quality image-segmentation pairs using a generative model. • The quality of a given test segmentation is then assessed by evaluating the difference from its projection onto the good-quality manifold. Introduction / Related Work / Methods and Experiments / Conclusion 02 [Overview of proposed framework]
- 5. Quality Control Introduction – Contribution • Propose a generic deep generative model-based framework which learns the manifold of good-quality segmentations for quality control on a per-case basis. • Implement the framework with a VAE and propose an iterative search strategy in the latent space. • Compare the performance of proposed method with regression-based methods on two different datasets. Introduction / Related Work / Methods and Experiments / Conclusion 03
- 6. Related Work Introduction / Related Work / Methods and Experiments / Conclusion 04 Learning-based quality control [1] Kohlberger, T., Singh, V., Alvino, C., Bahlmann, C., Grady, L.: Evaluating segmentation error without ground truth. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2012) 528–536 [2] Robinson, R., Oktay, O., Bai, W., Valindria, V.V., Sanghvi, M.M., Aung, N., Paiva, J.M., Zemrak, F., Fung, K., Lukaschuk, E., et al.: Real-time prediction of segmentation quality. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2018) 578–585 [3] Liu, F., Xia, Y., Yang, D., Yuille, A.L., Xu, D.: An alarm system for segmentation algorithm based on shape model. In: Proceedings of the IEEE International Conference on Computer Vision. (2019) 10652–10661 • [1] Proposed 42 hand-crafted features based on intensity and appearance and achieved an accuracy of 85% in detecting segmentation failure. • [2] developed a CNN-based method for real-time regression of the Dice similarity metric from image- segmentation pairs. • [3] used a variational auto-encoder (VAE) for learning the shape features of segmentation in an unsupervised manner.
- 7. Related Work Introduction / Related Work / Methods and Experiments / Conclusion 05 Registration-based quality control [4] Valindria, V.V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E.O., Rockall, A.G., Rueckert, D., Glocker, B.: Reverse classification accuracy: predicting segmentation performance in the absence of ground truth. IEEE transactions on medical imaging 36(8) (2017) 1597–1606 • [4] proposed the concept of reverse classification accuracy (RCA) to predict segmentation quality and achieved good performance on a large-scale cardiac MRI dataset.
- 8. Methods and Experiments Proposed Framework Introduction / Related Work / Methods and Experiments / Conclusion 06 • The proposed framework aims to find a good-quality segmentation s as a surrogate for GT. • The generative model G is trained to learn a mapping from the low-dimensional latent space to the good-quality manifold • The input image-segmentation pair I is projected to on the manifold through iterative search. • is the initial guess in the latent space and it converges to
- 9. Methods and Experiments Iterative Search in the latent space Introduction / Related Work / Methods and Experiments / Conclusion 07 • Develop an iterative search scheme in the latent space to find a surrogate segmentation for a given image-segmentation pair as input. • Find a closest surrogate segmentation on the good-quality manifold as an optimization problem.
- 10. Methods and Experiments Generative Model using VAE Introduction / Related Work / Methods and Experiments / Conclusion 08 • Employ the VAE. Image-segmentation pair (I,S) is encoded by E to follow a Gaussian distribution in the latent space. • At the training stage, ground-truth image-segmentation pairs are used to train VAE. • In the application stage, the VAE decoder is used as the generator for iterative search of the surrogate segmentation on the good-quality manifold. • Initial guess: from the encoder / Final Guess: , is calculated.
- 11. Methods and Experiments Generative Model using VAE Introduction / Related Work / Methods and Experiments / Conclusion 09
- 12. Methods and Experiments Experiments - Dataset Introduction / Related Work / Methods and Experiments / Conclusion 10 • UK Biobank dataset - Short-axis cardiac images at the end-diastolic (ED) frame of 1,500 subjects were obtained. • ACDC dataset - 100 subjects including a normal group and four pathology groups were obtained. • Comparison Methods: - Support vector regression(SVR) with 42 hand-crafted features about shape and appearance. - CNN regression network (ResNet-18 back-bone) with the image-segmentation pair as input.
- 13. Methods and Experiments Experiments Settings Introduction / Related Work / Methods and Experiments / Conclusion 11 • Experiment 1: UK Biobank - Besides the GT segmentations, generated poor-quality segmentations by attacking the segmentation model (White noise with different variance levels). Quality prediction was performed on the test set of the attacked segmentations • Experiment 2: ACDC - Deployed a UK Biobank trained segmentation model on ACDC dataset without fine-tuning. This reflects a real-world clinical setting, where segmentation failures would occur due to domain shift issues. * Focused on myocardium segmentation which is a challenging cardiac structure to segment and of high clinical relevance.
- 14. Methods and Experiments Experiments Introduction / Related Work / Methods and Experiments / Conclusion 12
- 15. Methods and Experiments Experiments Introduction / Related Work / Methods and Experiments / Conclusion 13
- 16. Methods and Experiments Experiments Introduction / Related Work / Methods and Experiments / Conclusion 14
- 17. Conclusion Introduction / Related Work / Methods and Experiments / Conclusion • Regression based QC methods are easily overfitted. The ACDC dataset consists of more pathological cases, whereas the UK Biobank comes from a general healthy population. • Proposed method maintained a high prediction accuracy against domain shift → Advantage of a generative model-based framework • Proposed method does not depend on specific segmentation models or types of segmentation failures. • Potential to be extended for quality control in different anatomical structures. 15