Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clustering from Ultrasound Images

Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain

Deep learning-based carotid media-adventitia and lumen-intima boundary segmentation from three-dimensional ultrasound images

Purpose: Quantification of carotid plaques has been shown to be important for assessing as well as monitoring the progression and regression of carotid atherosclerosis. Various metrics have been proposed and methods of measurements ranging from manual tracing to automated segmentations have also been investigated. Of those metrics, quantification of carotid plaques by measuring vessel-wall-volume (VWV) using the segmented media-adventitia (MAB) and lumen-intima (LIB) boundaries has been shown to be sensitive to temporal changes in carotid plaque burden. Thus, semi-automatic MAB and LIB segmentation methods are required to help generate VWV measurements with high accuracy and less user interaction. Methods: In this paper, we propose a semiautomatic segmentation method based on deep learning to segment the MAB and LIB from carotid three-dimensional ultrasound (3DUS) images. For the MAB segmentation, we convert the segmentation problem to a pixel-by-pixel classification problem. A dynamic convolutional neural network (Dynamic CNN) is proposed to classify the patches generated by sliding a window along the norm line of the initial contour where the CNN model is fine-tuned dynamically in each test task. The LIB is segmented by applying a region-of-interest of carotid images to a U-Net model, which allows the network to be trained end-to-end for pixel-wise classification. Results: A total of 144 3DUS images were used in this development, and a threefold cross-validation technique was used for evaluation of the proposed algorithm. The proposed algorithm-generated accuracy was significantly higher than the previous methods but with less user interactions. Comparing the algorithm segmentation results with manual segmentations by an expert showed that the average Dice similarity coefficients (DSC) were 96.46 ± 2.22% and 92.84 ± 4.46% for the MAB and LIB, respectively, while only an average of 34 s (vs 1.13, 2.8 and 4.4 min in previous methods) was required to segment a 3DUS image. The interobserver experiment indicated that the DSC was 96.14 ± 1.87% between algorithm-generated MAB contours of two observers\u27 initialization. Conclusions: Our results showed that the proposed carotid plaque segmentation method obtains high accuracy and repeatability with less user interactions, suggesting that the method could be used in clinical practice to measure VWV and monitor the progression and regression of carotid plaques

Developing an automatic brachial artery segmentation and bloodstream analysis tool using possibilistic C-means clustering from color doppler ultrasound images

Automatic segmentation of brachial artery and blood-flow dynamics are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose a software that is noise tolerant and fully automatic in segmentation of brachial artery from color Doppler ultrasound images. Possibilistic C-Means clustering algorithm is applied to make the automatic segmentation. We use HSV color model to enhance the contrast of bloodstream area in the input image. Our software also provides index of hemoglobin distribution with respect to the blood flow velocity for pathologists to proceed further analysis. In experiment, the proposed method successfully extracts the target area in 59 out of 60 cases (98.3%) with field expert’s verification

A Systematic Review and Meta-Analysis of the Incidence of Injury in Professional Female Soccer

The epidemiology of injury in male professional football is well documented and has been used as a basis to monitor injury trends and implement injury prevention strategies. There are no systematic reviews that have investigated injury incidence in women’s professional football. Therefore, the extent of injury burden in women’s professional football remains unknown. PURPOSE: The primary aim of this study was to calculate an overall incidence rate of injury in senior female professional soccer. The secondary aims were to provide an incidence rate for training and match play. METHODS: PubMed, Discover, EBSCO, Embase and ScienceDirect electronic databases were searched from inception to September 2018. Two reviewers independently assessed study quality using the Strengthening the Reporting of Observational Studies in Epidemiology statement using a 22-item STROBE checklist. Seven prospective studies (n=1137 professional players) were combined in a pooled analysis of injury incidence using a mixed effects model. Heterogeneity was evaluated using the Cochrane Q statistic and I2. RESULTS: The epidemiological incidence proportion over one season was 0.62 (95% CI 0.59 - 0.64). Mean total incidence of injury was 3.15 (95% CI 1.54 - 4.75) injuries per 1000 hours. The mean incidence of injury during match play was 10.72 (95% CI 9.11 - 12.33) and during training was 2.21 (95% CI 0.96 - 3.45). Data analysis found a significant level of heterogeneity (total Incidence, X2 = 16.57 P < 0.05; I2 = 63.8%) and during subsequent sub group analyses in those studies reviewed (match incidence, X2 = 76.4 (d.f. = 7), P <0.05; I2 = 90.8%, training incidence, X2 = 16.97 (d.f. = 7), P < 0.05; I2 = 58.8%). Appraisal of the study methodologies revealed inconsistency in the use of injury terminology, data collection procedures and calculation of exposure by researchers. Such inconsistencies likely contribute to the large variance in the incidence and prevalence of injury reported. CONCLUSIONS: The estimated risk of sustaining at least one injury over one football season is 62%. Continued reporting of heterogeneous results in population samples limits meaningful comparison of studies. Standardising the criteria used to attribute injury and activity coupled with more accurate methods of calculating exposure will overcome such limitations