The invention is applicable to the technical field of medical
image processing and
mixed reality, and provides a
laparoscopic surgery mixed reality navigation method based on
deep learning and dynamic
point tracking, which comprises the following steps: dynamically registering a three-dimensional model containing
kidney, tumor and vessel with an initial frame of a laparoscope through a
mixed reality alignment technology; the method comprises the following steps: constructing an operating
forceps motion sensing model based on a
time sequence deep neural network, realizing real-
time control and parameter locking of a three-dimensional model
pose, dynamically updating a two-dimensional feature
point set by adopting a multi-feature-point combined tracker, constructing a candidate
feature combination through a cross-quadrant sampling strategy, and constructing an operating
forceps motion sensing model; a candidate
feature combination is generated through a four-quadrant division and cross-regional sampling strategy, an optimal camera
pose parameter is generated in combination with a re-projection error and
pose continuity constraint, and an operation video is dynamically covered with a semitransparent three-dimensional model. The method can significantly enhance the
spatial perception capability of the
kidney anatomical structure, reduce the registration error of the
kidney in the three-dimensional integrated kidney structure model and the laparoscope video, and improve the navigation precision.