Abstract
In this paper, we present a technique for automatic detection and counting of Plasmodium vivax-infected red blood cells by means of a convolutional neural network and a feature- based counting process. Current approaches for object detection or counting often rely on prior knowledge of certain salient features of the to-be-identified objects or require time-consuming pre-processing. For this reason, many detection problems, for example infected cell counting, remain a manual task for trained professionals, leading to potentially high amounts of time between infection and diagnosis and treatment, which, in turn, can have lethal consequences. Using the BBBC041 data set, we annotated the ground truth (GT) of infected cells with circles in each image and then trained a convolutional neural network to predict these GTs from previously unseen cell images. Subsequently, the algorithm computes the number of cells using Canny edge detection and circular Hough Transform.
Supported by University of Osnabrück.
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Change history
24 September 2020
In the original version of the book, the name of the fourth author of Chapter 15 was spelt incorrectly, which has now been corrected and updated.
Notes
- 1.
The annotation of cells in the shape of circles provides an easy way to count overlapping GTs.
- 2.
The hyper-parameters had different optimal configurations for the different GTs.
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ED and AP contributed equally to the design, implementation and analysis of the research. AS and UK supervised the project and offered valuable feedback.
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Abbreviations
- CNN
-
Convolutional Neural Network
- GT
-
Ground Truth
- HCT
-
Hough Circular Transform
- P. vivax
-
Plasmodium vivax
- ReLU
-
Rectified Linear Units
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Doering, E., Pukropski, A., Krumnack, U., Schaffand, A. (2020). Automatic Detection and Counting of Malaria Parasite-Infected Blood Cells. In: Su, R., Liu, H. (eds) Medical Imaging and Computer-Aided Diagnosis. MICAD 2020. Lecture Notes in Electrical Engineering, vol 633. Springer, Singapore. https://doi.org/10.1007/978-981-15-5199-4_15
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DOI: https://doi.org/10.1007/978-981-15-5199-4_15
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