  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.
