Hierarchical Prediction of Registration Misalignment using a Convolutional LSTM
Application to Chest CT Scans
Hessam Sokooti (Leiden University Medical Center)
Sahar Yousefi (Leiden University Medical Center)
Mohamed S. Elmahdy (Leiden University Medical Center)
Boudewijn Lelieveldt (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
Marius Staring (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
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Abstract
In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: 'correct' 0-3 mm, 'poor' 3-6 mm and 'wrong' over 6 mm. Rather than a direct prediction, we propose a hierarchical approach, where the prediction is gradually refined from coarse to fine. Our solution is based on a convolutional Long Short-Term Memory (LSTM), using hierarchical misalignment predictions on three resolutions of the image pair, leveraging the intrinsic strengths of an LSTM for this problem. The convolutional LSTM is trained on a set of artificially generated image pairs obtained from artificial displacement vector fields (DVFs). Results on chest CT scans show that incorporating multi-resolution information, and the hierarchical use via an LSTM for this, leads to overall better F1 scores, with fewer misclassifications in a well-tuned registration setup. The final system yields an accuracy of 87.1%, and an average F1 score of 66.4% aggregated in two independent chest CT scan studies.
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