RESUMO
Background: For respiration induced tumor displacement during a radiation therapy, a common method to prevent the extra radiation is image-guided radiation therapy. Moreover, mask region-based convolutional neural networks (Mask R-CNN) is one of the state-of-the-art (SOTA) object detection frameworks capable of conducting object classification, localization, and pixel-level instance segmentation. Methods: We developed a novel ultrasound image tracking technology based on Mask R-CNN for stable tracking of the detected diaphragm motion and applied to the respiratory motion compensation system (RMCS). For training Mask R-CNN, 1800 ultrasonic images of the human diaphragm are collected. Subsequently, an ultrasonic image tracking algorithm was developed to compute the mean pixel coordinates of the diaphragm detected by Mask R-CNN. These calculated coordinates are then utilized by the RMCS for compensation purposes. The tracking similarity verification experiment of mask ultrasonic imaging tracking algorithm (M-UITA) is performed. Results: The correlation between the input signal and the signal tracked by M-UITA was evaluated during the experiment. The average discrete Fréchet distance was less than 4 mm. Subsequently, a respiratory displacement compensation experiment was conducted. The proposed method was compared to UITA, and the compensation rates of three different respiratory signals were calculated and compared. The experimental results showed that the proposed method achieved a 6.22% improvement in compensation rate compared to UITA. Conclusions: This study introduces a novel method called M-UITA, which offers high tracking precision and excellent stability for monitoring diaphragm movement. Additionally, it eliminates the need for manual parameter adjustments during operation, which is an added advantage.
RESUMO
BACKGROUND: The high incidence of Diabetes mellitus (DM) has become a serious challenge for the global epidemic. Increased blood glucose leads to abnormal ocular surface structure and metabolic disorder in patients. DM is a high-risk factor for dry eye disease (DED), with high incidence and increased difficulty in treatment. The disease can cause discomfort, visual impairment, tear film instability and ocular surface damage, and even cause corneal erosion in severe cases, which has a serious impact on people's daily life. Traditional Chinese Medicine (TCM) plays an important role in the evaluation and treatment of DM and its complications. However, whether TCM treatment could improve the treatment efficacy of DM suffering from DED remains poorly understood. OBJECTIVE: To investigate the curative effect of TCM for the alleviation of clinical symptoms in Diabetic patients with DED, and to evaluate its long-term efficacy. METHODS: This trial is a single-case randomized, single-blind, placebo-controlled study. A total of 12 subjects will be recruited in this trial. The trial is divided into three cycles, and one cycle has 2 treatment periods. There is a washout period at each adjacent treatment stage. TCM individualized treatment and placebo will be randomized during the treatment period. The test period will last for 29 weeks, with 4 weeks for each treatment period and 1 week for each washout period to minimize carryover effects. Subjects will be selected by the researcher strictly in accordance with the inclusion and exclusion criteria. The outcomes will evaluate the efficacy of treatment by changes in the various observation indicators. DISCUSSION: This study will realize a patient-centered outcome approach necessary to provide clinical researchers with the evidence that TCM treatment can effectively improve the objective indicators of the eye and systemic symptoms in Diabetic patients with DED. TRIAL REGISTRATION: This study has been registered at the Chinese Clinical Trial Registry (http://www.chictr.org.cn, No. ChiCTR1900024481), (October, 2019).