Improved Extraction of Objects From Urine Microscopy Images With Unsupervised Thresholding and Supervised U-Net Techniques.

We propose a novel unsupervised method for extracting objects from urine microscopy images and also applied U-net for extracting these objects. We fused these proposed methods with a known edge thresholding technique from an existing work on segmentation of urine microscopic images. Comparison between our proposed methods and the existing work showed that for certain object types the proposed unsupervised method with or without edge thresholding outperforms the other methods, while in other cases the U-net method with or without edge thresholding outperforms the other methods. Overall the proposed unsupervised method along with edge thresholding worked the best by extracting maximum number of objects and minimum number of artifacts. On a test dataset, the artifact to object ratio for the proposed unsupervised method was 0.71, which is significantly better than that of 1.26 for the existing work. The proposed unsupervised method along with edge thresholding extracted 3208 objects as compared to 1608 by the existing work. To the best of our knowledge this is the first application of Deep Learning for extraction of clinically significant objects in urine microscopy images.