New methods for automatic quality assessments of medical image registration

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As one of the most common tasks in computer vision and image processing, image registration is the procedure of aligning two pictures so that their corresponding features can be spatially matched. In the applications of radiological diagnosis, surgical planning, and medical image analysis, the requirement of accurate registration between medical scans is ubiquitous and crucial for the patient's safety, treatment outcomes, and reliability of image-based research into the mechanisms of diseases. Automatic registration algorithms are commonly used. However, sub-optimal results that may lead to adverse and even fatal consequences can still occur while quality control primarily relies on subjective visual inspection largely due to the absence of ground truths. Therefore, effective automatic techniques to assess and visualize registration errors and uncertainties are highly valuable, but are still under-explored. Currently, there are three main challenges in the assessment of medical image registration quality. First, the quality of a registration is often difficult to evaluate in the absence of ground truths (e.g., matching anatomical landmarks and structural segmentation between images), but these are time-consuming to produce and subject to rater-dependent variability. Second, automatic techniques in registration quality assessment for inter-modal and inter-contrast alignment are rare. Lastly, specialized methods are needed to quantify and visualize registration quality for deep-learning-based image registration algorithms to improve their interpretability. The proposed research program will tackle these challenges and develop novel techniques to provide efficient and robust assessment of medical image registration quality. More specifically, we will propose new methods for automatic anatomical landmark identification, devise learning-based techniques to directly predict registration errors, and establish novel frameworks that can quantify and visualize registration uncertainties for deep-learning-based registration algorithms. The proposed research will meet the urgent needs for robust methods to quantify and visualize image registration quality for the development and validation of new image registration algorithms. In the applications of patient care and disease analyses, the resulting methods are expected to provide fast feedback to the end users for the results from automatic image registration software to ensure the accuracy of disease diagnoses, patient safety in image-guided surgery, and reliability of big data analyses involving radiological scans.