Towards Direct Medical Image Analysis without Segmentation
This addresses the issue of computational waste and limited practicality in clinical applications for medical image analysis, though it appears incremental as it builds on emerging trends.
The paper tackles the problem of inefficient medical image analysis by proposing direct methods that bypass intermediate steps like segmentation, resulting in more clinically significant and resource-efficient solutions.
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 time-consuming, which causes a high waste of research resources. The advantages of direct methods stem from \textbf{1)} removal of intermediate steps, e.g., segmentation, tracking and registration; \textbf{2)} avoidance of user inputs and initialization; \textbf{3)} reformulation of conventional challenging problems, e.g., inversion problem, with efficient solutions.