Urine Microscopic Image Dataset
This addresses a data scarcity problem for researchers in medical imaging and urinalysis automation, but it is incremental as it provides a new dataset rather than a novel method.
The authors tackled the lack of publicly available urine microscopic image datasets by creating the UMID dataset with around 3700 cell annotations across three categories (RBC, pus, epithelial cells), making it publicly available to support further research.
Urinalysis is a standard diagnostic test to detect urinary system related problems. The automation of urinalysis will reduce the overall diagnostic time. Recent studies used urine microscopic datasets for designing deep learning based algorithms to classify and detect urine cells. But these datasets are not publicly available for further research. To alleviate the need for urine datsets, we prepare our urine sediment microscopic image (UMID) dataset comprising of around 3700 cell annotations and 3 categories of cells namely RBC, pus and epithelial cells. We discuss the several challenges involved in preparing the dataset and the annotations. We make the dataset publicly available.