The place of bladder wall thickness measurement in the evaluation protocol of the neurogenic bladder patient

2017 ◽  
Vol 16 (11) ◽  
pp. e2901
Author(s):  
C. Persu ◽  
D. Radavoi ◽  
N. Chirca ◽  
T.G. Dida ◽  
V. Jinga
Keyword(s):  
Neurogenic Bladder ◽  
Wall Thickness ◽  
Bladder Wall ◽  
Thickness Measurement ◽  
Bladder Wall Thickness ◽  
Evaluation Protocol ◽  
Wall Thickness Measurement

1202: The Value of Bladder Wall Thickness Measurement in the Assessment of Voiding Dysfunction

2005 ◽  
Vol 173 (4S) ◽  
pp. 326-326
Author(s):  
Lewis Chan ◽  
Jehan Titus ◽  
Vincent Tse ◽  
Ruth Collins
Keyword(s):  
Wall Thickness ◽  
Voiding Dysfunction ◽  
Bladder Wall ◽  
Thickness Measurement ◽  
Bladder Wall Thickness ◽  
Wall Thickness Measurement

545 Can bladder wall thickness measurement use for detecting bladder outlet obstruction?

2015 ◽  
Vol 14 (2) ◽  
pp. e545-e545a
Author(s):  
O. Guzel ◽  
Y. Aslan ◽  
M. Balci ◽  
A. Tuncel ◽  
T. Keten ◽  
...  
Keyword(s):  
Wall Thickness ◽  
Bladder Outlet Obstruction ◽  
Bladder Wall ◽  
Outlet Obstruction ◽  
Thickness Measurement ◽  
Bladder Wall Thickness ◽  
Wall Thickness Measurement

Re: Güzel et al: Can Bladder Wall Thickness Measurement Be Used for Detecting Bladder Outlet Obstruction?

Urology ◽  
2016 ◽  
Vol 88 ◽  
pp. 228
Author(s):  
Kawaljit Singh ◽  
Apul Goel ◽  
SN Sankhwar ◽  
Manoj Kumar ◽  
Ashok Gupta
Keyword(s):  
Wall Thickness ◽  
Bladder Outlet Obstruction ◽  
Bladder Wall ◽  
Outlet Obstruction ◽  
Thickness Measurement ◽  
Bladder Wall Thickness ◽  
Wall Thickness Measurement

Can Bladder Wall Thickness Measurement Be Used for Detecting Bladder Outlet Obstruction?

Urology ◽  
2015 ◽  
Vol 86 (3) ◽  
pp. 439-444 ◽  
Author(s):  
Özer Güzel ◽  
Yılmaz Aslan ◽  
Melih Balcı ◽  
Altuğ Tuncel ◽  
Tanju Keten ◽  
...  
Keyword(s):  
Wall Thickness ◽  
Bladder Outlet Obstruction ◽  
Bladder Wall ◽  
Outlet Obstruction ◽  
Thickness Measurement ◽  
Bladder Wall Thickness ◽  
Wall Thickness Measurement

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.

scholarly journals Subtle errors of bladder wall thickness measurement have a significant impact on the calculation of ultrasound-estimated bladder weight. A pilot study

2018 ◽  
Vol 20 (3) ◽  
pp. 292
Author(s):  
Vasileios I. Sakalis ◽  
Vasileios Sfiggas ◽  
Ioannis Vouros ◽  
Georgios Salpiggidis ◽  
Athanasios Papathanasiou ◽  
...  
Keyword(s):  
Wall Thickness ◽  
Diagnostic Tool ◽  
Measurement Errors ◽  
Bladder Wall ◽  
Thickness Measurement ◽  
Calculation Model ◽  
Lower Urinary Tract Dysfunction ◽  
Bladder Wall Thickness ◽  
Bladder Weight ◽  
D Model

Aims: Ultrasound-estimated bladder weight (UEBW), is an emerging diagnostic tool, which has been used in both males and females with lower urinary tract dysfunction. The currently acknowledged UEBW calculation methods rely on the accurate measurement of bladder wall thickness (BWT). We aim to identify if subtle errors in BWT measurement have a significant impact on UEBW calculations.Materials and methods: Twenty patients were randomly selected from an overactive bladder patient cohort. The primary endpoint was to identify the range of false BWT measurements outside which significant changes in UEBW calculation occur. We used the Kojima method and a semi-automatic 3-D model that is based on Chalana’s principle. Measurements were performed using the correct BWT and a series of faulty calculations from +0.5 mm to -0.5 mm using steps of 0.05 mm from true BWT. The effect of a fixed 0.5 mm BWT error was checked in bladder volumes above and below 250 ml and in three UEBW groups (<35 gr; 36-50 gr; >51gr).Results: BWT measurement errors above 0.25 mm cause statistically significant changes in UEWB calculation when a 3-D model is used and errors above 0.15 mm when Kojima’s method is used. At a fixed BWT error of 0.5 mm and bladder volume <250 ml, there is a 23.76% deviation from true UEBW, while at volumes >250 ml the deviation is 32.72%. The deviation is inversely proportional to the UEBW result, and heavier bladders deviate less.Conclusions: UEBW is a promising diagnostic tool, but small errors in BWT measurement might cause significant deviation from the true values. A 3-D calculation model appears to minimize such risks.

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