Image registration combines data from multiple imaging modalities or time points by aligning images. Existing 2D-3D registration methods often require manual intervention, introducing variability and increasing processing time, and rely on Computed Tomography (CT) imaging, which exposes patients to radiation. This study aims to develop an automated registration pipeline that ensures accurate registration between 3D Magnetic Resonance Imaging (MRI) and 2D fluoroscopic images by preprocessing techniques.

This study involved two registration experiments to evaluate the pipeline. The DiffDRR package was used with normalized cross-correlation (NCC) as similarity metric and gradient-based Adam optimizer for optimization. The first experiment validated the accuracy of registration between 3D MRI models (solid bone, empty shell, and filled shell) and 2D CT-derived digitally reconstructed radiographs (DRRs) with known rotations and translations as reference standard. The second experiment aimed to determine the most effective fluoroscopy preprocessing technique for aligning 3D MRI models with 2D fluoroscopic images. Preprocessing techniques for the fluoroscopic images included contrast enhancement via thresholding and normalization, and region of interest selection or segmentation.

Validation results showed smaller rotational and translational errors for both shell models compared to the solid bone model. In-plane errors for the shell models were 0.1-3.3mm and out-of-plane 15.8-55.0mm, while solid bone reported in-plane errors of 1.0-4.7mm and out-of-plane of 68.3-282.7mm. Registration between empty shell models and differently preprocessed fluoroscopic images showed that segmentation of the bones in fluoroscopic images yielded the highest NCC values. However, visual assessment revealed inconsistencies in plausible bone positioning.

This automated 2D-3D registration method shows potential for registration between 3D MRI and 2D fluoroscopic images of the knee joint. The current method requires further refinement, particularly in integrating soft tissue data, possibly via synthetic CTs, and adopting more anatomically sensitive metrics, such as mutual information or gradient correlation.

Recent years have shown a tremendous increase in the application of Artificial Intelligence to the field of radiology, often through the extraction and analysis of large numbers of quantitative features from medical images. These applications increase the demand for machine learning models to extract information from these images. To provide these models, improve their performance and reduce the time that experts have to spend on manually tuning them, the field of Automated Machine Learning (AutoML) aims to automate the design process of machine learning models by optimizing the selection of algorithms and their hyperparameters for each application. This work applies an AutoML approach to medical image classification, using a Bayesian optimization strategy to automatically optimize the selection of preprocessing and classification algorithms and their hyperparameters. Its performance is compared with the performance of a random search optimization strategy, evaluated on three datasets from three different clinical applications. The results show that the Bayesian optimization and the random search return models that achieve similar performance on the unseen test sets. We show that a random search with relatively few evaluations and a simple ensemble strategy is sufficient to achieve performance comparable to a more sophisticated and more computationally demanding Bayesian optimization approach, therefore validating the use of a random search optimization strategy in this medical image classification setting. All found models generalize poorly, with average F1-scores on the validation sets used for optimizing the models being at least 20\% lower than the average F1-scores on the unseen test sets. Finally, we further emphasize the difficulty to generalize in this setting, by showing that the differences between subsets of the evaluated datasets are large and that increasing the computation time of the optimization does not benefit the test set performance of the final solution.