Fig 1.
Measurements of three axial diameters on two-dimensional images.
a (Transverse section): Two points on the bladder contour with the longest distance on a line parallel to (or small slope against) the horizontal axis. b (Longitudinal section): Two points on the bladder contour with the longest distance. c: Two points on the bladder contour with the longest distance on a line orthogonal to line b.
Fig 2.
Methodological overview: Automated bladder volume measurement.
Users collect one transverse and longitudinal image each, and the subsequent processing is automated. Bladder area segmentation is performed by a deep learning algorithm, measurement of the three diameters is performed by ellipse fitting, and bladder volume is mathematically calculated as an ellipsoid. Even if the bladder protrudes from the view, the correct value can be determined by an artificial intelligence (AI) approach by training under expert advice.
Fig 3.
Scatter plots of the actual voided volume versus sonographer-calculated (manual) and AI-calculated (automatic) volumes.
Dotted horizontal lines indicate the approximate straight line of the regression equation shown in the square.
Table 1.
Confusion matrix of actual voided volume versus manually calculated and automatically calculated volumes (threshold: 100 ml).
Table 2.
Confusion matrix of actual voided volume versus manually calculated and automatically calculated volumes (threshold: 50 ml).
Fig 4.
(a, b) Transverse and longitudinal ultrasound (US) images of the same male aged approximately 20 years. (c, d) transverse and longitudinal US images of the same male aged approximately 30 years. Actual: Urine volume actually urinated by the subject. AI: Value of bladder urine volume estimated by automatic estimation tool. Yellow lines are detected by AI as bladder diameters.