In Vivo and Simultaneously Acquired Endoluminal Ultrasound Biomicroscopic and Colonoscopic Images of Inflamed Mouse Colon and Wall Thickness Measurement

2020 ◽  
Author(s):  
Rodrigo P. de Oliveira ◽  
Carine Belau de Castro ◽  
Anderson Weber Faletti Cunha ◽  
Rossana Colla Solleti ◽  
Helena Lobo Borges ◽  
...  
Keyword(s):  
Wall Thickness ◽  
Thickness Measurement ◽  
Endoluminal Ultrasound ◽  
Mouse Colon ◽  
Wall Thickness Measurement

Application of Automatic Ultrasonic Wall Thickness Measurement System in Nuclear Fuel Encrust Tube Billet Wall Abrasive Grinding

2012 ◽  
Vol 565 ◽  
pp. 662-667
Author(s):  
Yun Huang ◽  
Yong Sheng Chen ◽  
Wei Wan ◽  
Cao Yong Tang ◽  
Ming De Zhang
Keyword(s):  
Nuclear Fuel ◽  
Wall Thickness ◽  
Measurement System ◽  
Thickness Measurement ◽  
System Stability ◽  
Tube Billet ◽  
Wall Thickness Measurement ◽  
Main Components ◽  
Thickness Measurement System ◽  
The Way

This paper introduces an automatic ultrasonic wall thickness measurement system, which adopts the way that the tube billet is partially immersed in the water during the measurement, applied in wall abrasive grinding of nuclear fuel encrust tube billet. Meanwhile, the main components of the measurement system, as well as their characters and functions, are addressed. What’s more, the analysis of the factors, which influencing the system stability and measuring reliability, are also conducted, coupling with the system stability and measuring reliability demonstration.

scholarly journals Fully Automated Segmentation of Bladder Sac and Measurement of Detrusor Wall Thickness from Transabdominal Ultrasound Images

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4175
Author(s):  
Zeynettin Akkus ◽  
Bae Hyung Kim ◽  
Rohit Nayak ◽  
Adriana Gregory ◽  
Azra Alizad ◽  
...  
Keyword(s):  
Wall Thickness ◽  
Bladder Wall ◽  
Muscle Thickness ◽  
Ground Truth ◽  
Thickness Measurement ◽  
Ultrasound Images ◽  
Detrusor Muscle ◽  
Fast Method ◽  
Continuous Assessment ◽  
Wall Thickness Measurement

Ultrasound measurements of detrusor muscle thickness have been proposed as a diagnostic biomarker in patients with bladder overactivity and voiding dysfunction. In this study, we present an approach based on deep learning (DL) and dynamic programming (DP) to segment the bladder sac and measure the detrusor muscle thickness from transabdominal 2D B-mode ultrasound images. To assess the performance of our method, we compared the results of automated methods to the manually obtained reference bladder segmentations and wall thickness measurements of 80 images obtained from 11 volunteers. It takes less than a second to segment the bladder from a 2D B-mode image for the DL method. The average Dice index for the bladder segmentation is 0.93 ± 0.04 mm, and the average root-mean-square-error and standard deviation for wall thickness measurement are 0.7 ± 0.2 mm, which is comparable to the manual ground truth. The proposed fully automated and fast method could be a useful tool for segmentation and wall thickness measurement of the bladder from transabdominal B-mode images. The computation speed and accuracy of the proposed method will enable adaptive adjustment of the ultrasound focus point, and continuous assessment of the bladder wall during the filling and voiding process of the bladder.

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