UUnet: An effective cascade Unet for automatic segmentation of renal parenchyma

Although deep learning image segmentation technology has achieved good results in medical image processing, it is still challenging to segment renal parenchyma from diuretic renography. The diuretic nephrogram has the characteristics of obvious noise, poor image quality, unclear boundary and serious redundant information. It is difficult to accurately segment renal parenchyma directly using the classical Unet network. Therefore, we propose a cascaded network, i.e. a segment network that realize segmentation from coarse to fine. The coarse segmentation model is used to obtain the suggested area of the kidney in the diuretic renal image. The cascaded fine segmentation model is to segment the renal parenchyma from the suggested region of the kidney. Compared with the original Unet, the cascade network can reduce the noise interference to a large extent and get better segmentation performance of the renal parenchyma. The experiment showed that the dice coefficient increased by 9.78%, and the proposed network is efficient in the renal parenchyma segmentation.