No abstract

No abstract

No abstract

Background: Trigger finger is a common hand disease, which is caused by a mismatch in diameter between the tendon and the pulley. Ultrasound images are typically used to diagnose this disease, which are also used to guide surgical treatment. However, background noise and unclear tissue boundaries in the images increase the difficulty of the process. To overcome these problems, a computer-aided tool for the identification of finger tissue is needed. Results: Two datasets were used for evaluation: one comprised different cases of individual images and another consisting of eight groups of continuous images. Regarding result similarity and contour smoothness, our proposed deeply supervised dilated fully convolutional DenseNet (D2FC-DN) is better than ATASM (the state-of-art segmentation method) and representative CNN methods. As a practical application, our proposed method can be used to build a tendon and synovial sheath model that can be used in a training system for ultrasound-guided trigger finger surgery. Conclusion: We proposed a D2FC-DN for finger tendon and synovial sheath segmentation in ultrasound images. The segmentation results were remarkably accurate for two datasets. It can be applied to assist the diagnosis of trigger finger by highlighting the tissues and generate models for surgical training systems in the future. Methods: We propose a novel finger tendon segmentation method for use with ultrasound images that can also be used for synovial sheath segmentation that yields a more complete description for analysis. In this study, a hybrid of effective convolutional neural network techniques are applied, resulting in a deeply supervised dilated fully convolutional DenseNet (D2FC-DN), which displayed excellent segmentation performance on the tendon and synovial sheath.

The spine is one of the most important parts of the human body. Spine diseases owning to aging or injuries would be treated by spine surgeries. However, the surgeries are highly dangerous and the surgical training can increase the success rate. In this paper, an interactive surgical simulation system for the training of pedicle screw implantation is introduced. We propose an automatic and efficient method to segment the vertebra from the CT images for building the vertebra model. The mean shift method which can cluster points having similar property into groups is used in data clustering. The clustered groups related to the bone region are chosen as the initial model. And then, we apply the Chan-Vese level set method which refines the vertebra contour by using the concepts of statistics. After segmentation, we then apply the marching cube algorithm to generate the 3D vertebra model, and pass it to a virtual surgical environment. A haptic device is adopted to interact with this model to fulfill the surgical simulation in the virtual environment. The average dice coefficient of the segmentation results is 0.92.