Blood smear is a crucial diagnostic aid. Quantification of both solitary and overlapping erythrocytes within these smears, directly from their whole slide images (WSIs), remains a challenge. Existing software designed to accomplish the computationally extensive task of hematological WSI analysis is too expensive and is widely unavailable. We have thereby developed a fully automated software targeted for erythrocyte detection and quantification from WSIs. We define an optimal region within the smear, which contains cells that are neither too scarce/damaged nor too crowded. We detect the optimal regions within the smear and subsequently extract all the cells from these regions, both solitary and overlapped, the latter of which undergoes a clump splitting before extraction. The performance was systematically tested on 28 WSIs of blood smears obtained from 13 different species from three classes of the subphylum vertebrata including birds, mammals, and reptiles. These data pose as an immensely variant erythrocyte database with diversity in size, shape, intensity, and textural features. Our method detected ∼3.02 times more cells than that detected from the traditional monolayer and resulted in a testing accuracy of 99.14% for the classification into their respective class (bird, mammal, or reptile) and a testing accuracy of 84.73% for the classification into their respective species. The results suggest the potential employment of this software for the diagnosis of hematological disorders, such as sickle cell anemia.
We present a rapid, scalable, and high throughput computational pipeline to accurately detect and segment the glomerulus from renal histopathology images with high precision and accuracy. Our proposed method integrates information from fluorescence and bright-field microscopy imaging of renal tissues. For computation, we exploit the simplicity, yet extreme robustness of Butterworth bandpass filter to extract the glomeruli by utilizing the information inherent in the renal tissue stained with immunofluorescence marker sensitive at blue emission wavelength as well as tissue auto-fluorescence. The resulting output is in-turn used to detect and segment multiple glomeruli within the fieldof-view in the same tissue section post-stained with histopathological stains. Our approach, optimized over 40 images, produced a sensitivity/specificity of 0.95/0.84 on n = 66 test images, each containing one or more glomeruli. The work not only has implications in renal histopathology involving diseases with glomerular structural damages, which is vital to track the progression of the disease, but also aids in the development of a tool to rapidly generate a database of glomeruli from whole slide images, essential for training neural networks. The current practice to detect glomerular structural damage is by the manual examination of biopsied renal tissues, which is laborious, time intensive and tedious. Existing automated pipelines employ complex neural networks which are computationally extensive, demand expensive highperformance hardware and require large expert-annotated datasets for training. Our automated method to detect glomerular boundary will aid in rapid extraction of glomerular compartmental features from large renal histopathological images.