BLADDER VOLUME ESTIMATION DEEP LEARNING ALGORITHM USING DEPTH DEPENDENT COEFFICIENTS OF ULTRASOUND SIGNALS

Bladder volume estimation in patients with dysuria is performed through ultrasound imaging. Estimation of bladder volume with bladder ultrasound images differs from the actual volume by an average of 18% when the bladder is assumed to have a spherical shape without considering the difference in a bladder shape along a bladder volume. To overcome this issue, we demonstrate a deep learning-based bladder volume estimation network that is capable of reducing volume estimation errors as the shape of the bladder changes. The proposed network synthesizes a few scanline images into an ultrasound image with a large number of scanlines using the combination of GAN(Pix2Pix) and U-Net architectures. The network shows an accuracy of 93% in terms of IoU, demonstrating the applicability of the bladder ultrasound wearable system for the segmentation of bladder regions with a few scanlines.