Simulations can mimic the environment that refers to the surgery operation to improve the technical skills of the trainees. In this paper, we designed a new cardiac surgery simulative training system. The isolated pig heart was selected as the heart model. A mechanical device was designed to achieve the beating of heart model. At the same time, adjusting frequencies of mechanical movement could change the rating of heartbeat. In order to validate the rationality of the system, 12 non-medical specialty students and 12 medical specialty students were divided into two groups, which consecutively accepted seven-days of training for off-pump coronary artery bypass grafting using the cardiac surgery simulative training system. The time for completing bypass grafting before and after training were recorded. And the bridging outcomes of each trainee were assessed by 3 surgery cardiac surgeons using the object structured assessments of technical skill (OSATS) criteria. After training, each trainee could finish the bypass suturing in a shorter time than before training, and the scores of each trainee assessed by OSATS criteria were also improved. The results showed that the cardiac surgery simulative training system had better training effect in improving the surgical techniques, operation skills and proficiency of surgical instruments of trainees.
Magnetic resonance (MR) imaging is an important tool for prostate cancer diagnosis, and accurate segmentation of MR prostate regions by computer-aided diagnostic techniques is important for the diagnosis of prostate cancer. In this paper, we propose an improved end-to-end three-dimensional image segmentation network using a deep learning approach to the traditional V-Net network (V-Net) network in order to provide more accurate image segmentation results. Firstly, we fused the soft attention mechanism into the traditional V-Net's jump connection, and combined short jump connection and small convolutional kernel to further improve the network segmentation accuracy. Then the prostate region was segmented using the Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset, and the model was evaluated using the dice similarity coefficient (DSC) and Hausdorff distance (HD). The DSC and HD values of the segmented model could reach 0.903 and 3.912 mm, respectively. The experimental results show that the algorithm in this paper can provide more accurate three-dimensional segmentation results, which can accurately and efficiently segment prostate MR images and provide a reliable basis for clinical diagnosis and treatment.