Uncertainty estimation in medical image registration

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Perustieteiden korkeakoulu | Master's thesis
Security and Cloud Computing
Degree programme
Master’s Programme in Security and Cloud Computing (SECCLO)
This thesis explores uncertainty estimation in deep-learning medical image registration. Image registration is an important part of medical imaging that aligns the images into one coordinate space to retrieve more information. Traditionally, image registration is performed manually. However, this approach is time-consuming and not accurate. Though software solutions improve registration, this process is still far from being accurate and effective. Deep learning models have shown promising results in automating this task, but lack of uncertainty estimation poses a limitation to implementing these models into clinical practice. Uncertainty indicates the level of confidence of the model in the output. This the- sis analyses the methods of uncertainty estimation and their suitability for medical image registration. The ensemble method is chosen for the project because this method can be applied to existing models, provides clear and transparent uncertainty estimation, and is easily scalable. Ensemble methods involve training several models with different initial states or architectures and combining their predictions to make more robust and accurate output. The ensemble of models allows uncertainty quantification through the measurement of standard deviation. As a result, it is possible to mark the regions with high uncertainty on a combined output deformed image. The results of uncertainty estimation are evaluated via several tests. Firstly, they are compared to the results of a probabilistic model. Though an ensemble and a probabilistic model return similar predictions, the ensemble is much better calibrated. In addition, a user study is conducted. In 70% of the cases, the opinion of the human experts coincides with the ensemble prediction. In conclusion, the results of tests conducted on the prediction of the ensemble show that ensemble methods are useful for uncertainty estimation in medical image registration. These methods can potentially enhance the accuracy and robustness of medical image registration.
Marttinen, Pekka
Thesis advisor
Honkamaa, Joel
ensemble, uncertainty estimation, image registration, medical images, deep learning
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