CT is an essential tool in modern medical diagnostics and plays a pivotal role in the healthcare industry. Of which, the detection method has a great impact on the imaging effect and diagnostic effect. Regular calibration and verification of detection equipment can ensure stable performance, reduce errors, and ensure clarity and accuracy of imaging. At present, manual measurement is the primary method used in most regions of China. This approach not only results in laborious and inefficient work but also makes it vulnerable to subjective interference from staff members. It does not support effective quality control for CT scans and presents considerable obstacles for evaluating the quality of medical equipment. Based on these problems, this paper uses MATLAB to design and implement automated measurement of CT images.
CT plays an irreplaceable role in modern medical diagnosis, and their quality directly affects doctors' judgment of diseases. Therefore, realizing the quality control of CT images to ensure that the image quality meets the diagnostic standards is an important measure to guarantee the medical quality and patient safety. In this paper, MATLAB is used as a tool to realize the automated detection of CT quality control, and the detection items include CT number of water, noise and layer thickness. The program is written to replace the manual detection steps to realize the measurement automation, and the interface is designed to form an APP through APP Designer. Experiments show that the automatic detection results of this software are more accurate and less time-consuming than the traditional way, which can be used for CT quality control detection.
The paravertebral muscle is a vital structure that maintains the stability of the lumbar spine. The decrease in muscle mass and the increase in fat content of the paravertebral muscle are closely related to the occurrence of various lumbar diseases. The degeneration of the paravertebral muscles is associated with various diseases, such as low back pain, degenerative scoliosis, osteoporosis, and so forth. At present, although a large number of studies have reported on vertebral computed tomography (CT) image segmentation, studies on paravertebral muscle segmentation are few. This study aimed to achieve the segmentation of the muscle region in vertebral CT images to provide clinically feasible index observation data for the diagnosis, treatment, and prognosis of related diseases. The traditional segmentation method is time-consuming and laborious. Also, the segmentation results vary significantly due to the different levels of experience of clinicians. Further, the repeatability is poor. This study used the automatic image segmentation model based on a deep learning algorithm, namely the U-net network model, to achieve vertebral muscle segmentation in CT images. The average Dice coefficient reached 0.9178, indicating a good segmentation effect of the segmentation model based on the U-net network. Based on the results of paravertebral muscle segmentation, automatic measurement and analysis of the cross-sectional area, paravertebral muscle density, and degree of fat infiltration can be further realized, there by guaranteeing the prognosis of patients with spinal diseases.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.