Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images
This work addresses the problem of quantifying atrial fibrosis for medical diagnosis, representing an incremental improvement in domain-specific image analysis.
The paper tackled automated segmentation and quantification of left atrial fibrosis and scars from cardiac MRI images, achieving an accuracy of 0.809±0.150 and Dice score of 0.556±0.187, with statistically better performance compared to conventional methods.
We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition session. We formulate the joint distribution of images using a multivariate mixture model (MvMM), and employ the maximum likelihood estimator (MLE) for texture classification of the images simultaneously. The MvMM can also embed transformations assigned to the images to correct the misregistration. The iterated conditional mode algorithm is adopted for optimization. This method first extracts the anatomical shape of the LA, and then estimates a prior probability map. It projects the resulting segmentation onto the LA surface, for quantification and analysis of scarring. We applied the proposed method to 36 clinical data sets and obtained promising results (Accuracy: $0.809\pm .150$, Dice: $0.556\pm.187$). We compared the method with the conventional algorithms and showed an evidently and statistically better performance ($p<0.03$).