Affiliations: [a] College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail: [email protected] | [b] Faculty of Science and Technology, University of Macau, Taipa, Macau, China. E-mail: [email protected] | [c] Department of Pathology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China. E-mail: [email protected] | [d] Department of Nephrology, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi, China. E-mail: [email protected]
Abstract: In the diagnosis of chronic kidney disease, glomerulus as the blood filter provides important information for an accurate disease diagnosis. Thus automatic localization of the glomeruli is the necessary groundwork for future auxiliary kidney disease diagnosis, such as glomerular classification and area measurement. In this paper, we propose an efficient glomerular object locator in kidney whole slide image(WSI) based on proposal-free network and dynamic scale evaluation method. In the training phase, we construct an intensive proposal-free network which can learn efficiently the fine-grained features of the glomerulus. In the evaluation phase, a dynamic scale evaluation method is utilized to help the well-trained model find the most appropriate evaluation scale for each high-resolution WSI. We collect and digitalize 1204 renal biopsy microscope slides containing more than 41000 annotated glomeruli, which is the largest number of dataset to our best knowledge. We validate the each component of the proposed locator via the ablation study. Experimental results confirm that the proposed locator outperforms recently proposed approaches and pathologists by comparing F1 and run time in localizing glomeruli from WSIs at a resolution of 0.25 μm/pixel and thus achieves state-of-the-art performance. Particularly, the proposed locator can be embedded into the renal intelligent auxiliary diagnosis system for renal clinical diagnosis by localizing glomeruli in high-resolution WSIs effectively.
