Localization of prostatic tumor's infection based on normalized mutual information MRI image segmentation

Enhancing the depth perception of DSA images with 2D-3D registration

Objective

Today, cerebrovascular disease has become an important health hazard. Therefore, it is necessary to perform a more accurate and less time-consuming registration of preoperative three-dimensional (3D) images and intraoperative two-dimensional (2D) projection images which is very important for conducting cerebrovascular disease interventions. The 2D-3D registration method proposed in this study is designed to solve the problems of long registration time and large registration errors in 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) images.

Methods

To make a more comprehensive and active diagnosis, treatment and surgery plan for patients with cerebrovascular diseases, we propose a weighted similarity measure function, the normalized mutual information-gradient difference (NMG), which can evaluate the 2D-3D registration results. Then, using a multi-resolution fusion optimization strategy, the multi-resolution fused regular step gradient descent optimization (MR-RSGD) method is presented to attain the optimal value of the registration results in the process of the optimization algorithm.

Result

In this study, we adopt two datasets of the brain vessels to validate and obtain similarity metric values which are 0.0037 and 0.0003, respectively. Using the registration method proposed in this study, the time taken for the experiment was calculated to be 56.55s and 50.8070s, respectively, for the two sets of data. The results show that the registration methods proposed in this study are both better than the Normalized Mutual (NM) and Normalized Mutual Information (NMI).

Conclusion

The experimental results in this study show that in the 2D-3D registration process, to evaluate the registration results more accurately, we can use the similarity metric function containing the image gray information and spatial information. To improve the efficiency of the registration process, we can choose the algorithm with gradient optimization strategy. Our method has great potential to be applied in practical interventional treatment for intuitive 3D navigation.

Intelligent Algorithm-Based Magnetic Resonance Imaging in Radical Gastrectomy under Laparoscope

The study focused on the influence of intelligent algorithm-based magnetic resonance imaging (MRI) on short-term curative effects of laparoscopic radical gastrectomy for gastric cancer. A convolutional neural network- (CNN-) based algorithm was used to segment MRI images of patients with gastric cancer, and 158 subjects admitted at hospital were selected as research subjects and randomly divided into the 3D laparoscopy group and 2D laparoscopy group, with 79 cases in each group. The two groups were compared for operation time, intraoperative blood loss, number of dissected lymph nodes, exhaust time, time to get out of bed, postoperative hospital stay, and postoperative complications. The results showed that the CNN-based algorithm had high accuracy with clear contours. The similarity coefficient (DSC) was 0.89, the sensitivity was 0.93, and the average time to process an image was 1.1 min. The 3D laparoscopic group had shorter operation time (86.3 ± 21.0 min vs. 98 ± 23.3 min) and less intraoperative blood loss (200 ± 27.6 mL vs. 209 ± 29.8 mL) than the 2D laparoscopic group, and the difference was statistically significant (P < 0.05). The number of dissected lymph nodes was 38.4 ± 8.5 in the 3D group and 36.1 ± 6.0 in the 2D group, showing no statistically significant difference (P > 0.05). At the same time, no statistically significant difference was noted in postoperative exhaust time, time to get out of bed, postoperative hospital stay, and the incidence of complications (P > 0.05). It was concluded that the algorithm in this study can accurately segment the target area, providing a basis for the preoperative examination of gastric cancer, and that 3D laparoscopic surgery can shorten the operation time and reduce intraoperative bleeding, while achieving similar short-term curative effects to 2D laparoscopy.