Experimental surgical navigation systems have been reported being used in laparoscopic surgery, however, accurate registration in the surgical navigation is very challenging due to vessel deformation. We aim to build a deformable model based on the preoperative CT images to improve the matching accuracy by using the finite element method (FEM). Enhanced CT scans before and after the left gastric artery (LGA) pulled up were performed for FEM model and ground-truth generating in a pig experiment, respectively. An ANSYS software was used to simulate the FEM model of the vessel after pulled up according to the need for the laparoscopic gastrectomy. The central line (Line B) of the FEM model and central line (Line A) of the ground-truth were drawn and compared with each other. On the basis of material and parameters acquired from the animal experiment, we built a perigastric vessels FEM model of the patient with gastric cancer and evaluated its accuracy in surgical scene of laparoscopic gastrectomy. In animal experiment, the average distance between the two central lines is 6.467mm while the average distance between the closest points of them is 3.751mm. In surgical scene of laparoscopic gastrectomy, superimposing the FEM model onto the 2D laparoscopic image demonstrated a good coincidence. In this study, we built a deformable vessel model based on the preoperative CT images which may improve the matching accuracy and supply a referable way for further research of the deformation matching in the laparoscopic gastrectomy navigation.
Computer-Aided Diagnosis of masses in mammograms is an important indicator of breast cancer. The use of retrieval systems in breast examination is increasing gradually. In this respect, the method of exploiting the vocabulary tree framework and the inverted file in the mammographic masse retrieval have been proved high accuracy and excellent scalability. However it just considered the features in each image as a visual word and had ignored the spatial configurations of features. It greatly affect the retrieval performance. To overcome this drawback, we introduce the geometric verification method to retrieval in mammographic masses. First of all, we obtain corresponding match features based on the vocabulary tree framework and the inverted file. After that, we grasps the main point of local similarity characteristic of deformations in the local regions by constructing the circle regions of corresponding pairs. Meanwhile we segment the circle to express the geometric relationship of local matches in the area and generate the spatial encoding strictly. Finally we judge whether the matched features are correct or not, based on verifying the all spatial encoding are whether satisfied the geometric consistency. Experiments show the promising results of our approach.