Automatic initialization and quality control of large-scale cardiac MRI segmentations

Abstract

Continuous advances in imaging technologies enable ever more comprehensive phenotyping of human anatomy and physiology. Concomitant reduction of imaging costs has resulted in widespread use of imaging in large clinical trials and population imaging studies. Magnetic Resonance Imaging (MRI), in particular, offers one-stop-shop multidimensional biomarkers of cardiovascular physiology and pathology. A wide range of analysis methods offer sophisticated cardiac image assessment and quantification for clinical and research studies. However, most methods have only been evaluated on relatively small databases often not accessible for open and fair benchmarking. Consequently, published performance indices are not directly comparable across studies and their translation and scalability to large clinical trials or population imaging cohorts is uncertain. Most existing techniques still rely on considerable manual intervention for the initialization and quality control of the segmentation process, becoming prohibitive when dealing with thousands of images. The contributions of this paper are three-fold. First, we propose a fully automatic method for initializing cardiac MRI segmentation, by using image features and random forests regression to predict an initial position of the heart and key anatomical landmarks in an MRI volume. In processing a full imaging database, the technique predicts the optimal corrective displacements and positions in relation to the initial rough intersections of the long and short axis images. Second, we introduce for the first time a quality control measure capable of identifying incorrect cardiac segmentations with no visual assessment. The method uses statistical, pattern and fractal descriptors in a random forest classifier to detect failures to be corrected or removed from subsequent statistical analysis. Finally, we validate these new techniques within a full pipeline for cardiac segmentation applicable to large-scale cardiac MRI databases. The results obtained based on over 1200 cases from the Cardiac Atlas Project show the promise of fully automatic initialization and quality control for population studies.

Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images

“…Last but not least, for clinical deployment, it is necessary to alert users when failure happens. In this regard, future work can be integrating the segmentation approach with an automatic quality control module, providing automatic segmentation assessment [e.g., estimated segmentation scores (39), model uncertainty maps (40)] to clinicians for further verification and refinement.…”

Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation

“…Flagged images can then be either re-processed with revised parameters or discarded from subsequent statistical analyses. We incorporate a segmentation quality assessment approach presented by Albà et al (2018). The SQA module uses a random forest classifier trained to distinguish between successful and unsuccessful segmentations based on intensity features around the blood pool and myocardial boundaries.…”

“…To automatically initialise the model, we use the method proposed by Albà et al (2018) with a further step to improve bi-ventricular model initialisation. First, the location of the LV is determined via a rough estimate of the intersection of slices from the SAX and LAX views.…”

Editorial: Current and Future Role of Artificial Intelligence in Cardiac Imaging

“…Here, it is worth listing a few preliminary works, such as automated quality control of CMR images using a deep learning approach to identify suboptimal image contrast or heart coverage (8). Other works have instead focused on quality control of the final image segmentation results using classical AI (9) or neural networks (10). Another area that may benefit from AI is "image-based computational cardiology, " which builds patient-specific digital models of the heart to simulate treatment response.…”