Automatic cyst and kidney segmentation in autosomal dominant polycystic kidney disease: Comparison of U-Net based methods

Autosomal Dominant Polycystic Kidney Disease is a genetic disease that causes uncontrolled growth of fluidfilled cysts in the kidney. Kidney enlargement resulting from the expansion of cysts is continuous and often associated with decreased renal function and kidney failure. Mouse and rat models are necessary to discover new drugs able to halt the progression of the disease. The analysis of the effects of pharmacological interventions in these models is based on renal morphology and quantification of changes in total renal volume and cyst volume. This requires a proper, reproducible and fast segmentation of the kidney images. We propose a set of fully convolutional networks for kidney and cyst segmentation in micro-CT images, based on the U-Net architecture, to compare them and analyze which ones perform better on contrast-enhanced micro-CT images from normal rats and rats with Autosomal Dominant Polycystic Kidney Disease. Networks have been tested on a series images, and the performance has been evaluated in terms of Intersection over Union and Dice coefficients. Results showed that the best performing networks are the U-Net in which a batch normalization layer is applied after each pair of 3 x 3 convolutions, and the U-Net in which convolutional layers are replaced by inception blocks. Results also showed accurate cyst-to-kidney volume ratios obtained from the segmented images, which is one of main metrics of interest. Finally, segmentation performance has been found to be stable as the images in the training set vary. Therefore, the proposed automatic methodology is suitable and immediately applicable to segment cysts and kidney from micro-CT images, and directly provides the cyst-to-kidney volume ratio.

Automated summary

This study proposes a method for kidney and cyst segmentation in micro-CT images of mice with Autosomal Dominant Polycystic Kidney Disease and normal mice using fully convolutional networks. Different network architectures are compared and evaluated based on Intersection over Union and Dice coefficients. The results show that the U-Net with a batch normalization layer after each pair of 3 x 3 convolutions, and the U-Net with convolutional layers replaced by inception blocks perform the best.