10 April 2018 Automated erythrocyte detection and classification from whole slide images
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Abstract
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.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Darshana Govind, Brendon R. Lutnick, John E. Tomaszewski, Pinaki Sarder, "Automated erythrocyte detection and classification from whole slide images," Journal of Medical Imaging 5(2), 027501 (10 April 2018). https://doi.org/10.1117/1.JMI.5.2.027501 Submission: Received 29 November 2017; Accepted 19 March 2018
Submission: Received 29 November 2017; Accepted 19 March 2018
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