Towards Direct Medical Image Analysis without Segmentation

Direct methods have recently emerged as an effective and efficient tool in automated medical image analysis and become a trend to solve diverse challenging tasks in clinical practise. Compared to traditional methods, direct methods are of much more clinical significance by straightly targeting to the final clinical goal rather than relying on any intermediate steps. These intermediate steps, e.g., segmentation, registration and tracking, are actually not necessary and only limited to very constrained tasks far from being used in practical clinical applications; moreover they are computationally expensive and timeconsuming, which causes a high waste of research resources. The advantages of direct methods stem from 1) removal of intermediate steps, e.g., segmentation, tracking and registration; 2) avoidance of user inputs and initialization; 3) reformulation of conventional challenging problems, e.g., inversion problem, with efficient solutions. Direct methods in medical image analysis scenarios are defined as a series of methodologies that estimate clinical measurements directly from medical imaging data without relying on any unnecessary, intermediate steps. The principle behind direct methods is that there are intrinsic relationship existing between medical images and clinical measurements; these relationship can be directly extracted and modeled to estimate clinical measurements for diagnosis and prognosis without relying on any intermediate stages. Direct methods have recently demonstrated its great effectiveness and efficiency in many aspects for direct medical image analysis, especially on two important applications: volume estimation and functional analysis. Direct estimation of cardiac volumes without segmentation has shown remarkable effectiveness due to its great advantages over conventional segmentation based methods. For more than 20 years, cardiac volume estimation has long been suffering from the intermediate, unreliable and even intractable segmentation steps in traditional methods. Segmentation has only been focused on a single ventricle, e.g., the left ventricle (LV) on a bi-ventricular view for a long time, and recently started to work on the right ventricle (RV), which remains unsolved, not to mention joint bi-ventricles, i.e., LV and RV, and even more challenging four chambers, i.e., LV, RV, LA and RA. All the four ventricular volumes, however, are routinely and intensively used in clinical practise for cardiac functional analysis.