A systematic review of multi-slice and multi-frame descriptors in cardiac MRI exams. (June 2022) - Delmondes, Pedro H． M．; Nunes, Fátima L． S．
Highlights: Descriptors quantify the cardiac LV shape, texture, motion and clinical parameters. Descriptors are built based on image processing, AI and Deformable Models techniques. Diagnosis of cardiac diseases have been the main goal of the papers. Public datasets availability to compare different techniques is scarce. Standard metrics are used across the domain, regardless of the descriptor's type. Abstract: Computer-Aided Diagnosis systems have been developed to help medical professional in their decision making routines towards a more accurate diagnosis. These systems process medical exams such as Magnetic Resonance (MRI) in order to quantify meaningful features. These can be used with similarity-measuring techniques in a Content-Based Image Retrieval context, or inputted into a machine learning classifier in order to support early disease detection. For cardiac MRIs, single slice descriptors have been proposed in the two-dimensional domain, shape descriptors have been proposed in the three-dimensional domain, and previous reviews have mapped these two descriptor categories. Nonetheless, no systematic review on these descriptors have looked at full cardiac MRI images sets. We have reviewed the literature by searching for descriptors that consider the whole slice set (multi-slice) or frames (multi-frame) in cardiac MRI exams. We discuss descriptors and techniques, the datasets that were used, and the different evaluation metrics. Finally, we highlight literature gaps and research opportunities. Graphical abstract: Image, graphical abstract