  Landmark Localization plays a very important role in processing medical
images as well as in disease identification. However, In medical field, it's a
challenging task because of the complexity of medical images and the high
requirement of accuracy for disease identification and treatment.There are two
dominant ways to regress landmark coordination, one using the full
convolutional network to regress the heatmaps of landmarks , which is a complex
way and heatmap post-process strategies are needed, and the other way is to
regress the coordination using CNN + Full Connective Network directly, which is
very simple and faster training , but larger dataset and deeper model are
needed to achieve higher accuracy. Though with data augmentation and deeper
network it can reach a reasonable accuracy, but the accuracy still not reach
the requirement of medical field. In addition, a deeper networks also means
larger space consumption. To achieve a higher accuracy, we contrived a new
landmark regression method which combing heatmap regression and direct
coordinate regression base on probability methods and system control theory.
