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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
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.
References
Alemu, M., Tadesse, D., Hailu, T., Mulu, W., Derbie, A., Hailu, T., Abera, B.: Performance of Laboratory Professionals Working on Malaria Microscopy in Tigray, North Ethiopia. J. Parasitol. Res. (2017)
Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Interactive object counting. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2014)
Bewes, J., Suchowerska, N., Mckenzie, D.: Automated cell colony counting and analysis using the circular hough image transform algorithm (chita). Phys. Med. Biol. 53, 5991–6008 (2008)
Bradski, G.: The OpenCV Library. Dr. Dobb’s J. Softw. Tools (2000)
Centers for Disease Control and Prevention: Malaria Disease (2019). https://www.cdc.gov/malaria/about/disease.html
Centers for disease control and prevention: treatment of malaria: guidelines for clinicians (united states) (2019). https://www.cdc.gov/malaria/diagnosis_treatment/clinicians1.html
Coulter, W.H.: Means for counting particles suspended in a uid. US2656508A (1949)
Hung, J., Goodman, A., Lopes, S., Rangel, G., Ravel, D., Costa, F., Marti, M., Carpenter, A.E.: Applying faster R-CNN for Object detection on malaria images. In: CVPR (2017)
Imtiaz, S., Drohlia, M., Nasir, K., Hussain, M., Ahmad, A.: Morbidity and mortality associated with Plasmodium vivax and Plasmodium falciparum infection in a tertiary care kidney hospital. Saudi J. Kidney Dis. Transpl. 26(6), 1169–1176 (2015)
Khan, M.I., Acharya, B., Singh, B.K., Soni, J.: Content based image retrieval approaches for detection of malarial parasite in blood images. Int. J. Biometrics Bioinform. 5(2), 97–110 (2011)
Khatri, K.M., Ratnaparkhe, V.R., Ph, D., Agrawal, S.S., Bhalchandra, A.S.: Image processing approach for malarial parasite identification. Int. J. Comput. Appl. (IJCA), 5–7 (2013)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. arXiv preprint (2015)
Ljosa, V., Sokolnicki, K.L., Carpenter, A.E.: Annotated high-throughput microscopy image sets for validation. Nat. Methods 9(7), 637 (2012)
Mitiku, K., Getahun, M., Gelaw, B.: The reliability of blood film examination for malaria at the peripheral health unit. Ethiopian J. Health Dev. 17(3), 197–204 (2003)
De Solórzano, C.O., Rodriguez, E.G., Jones, A., Pinkel, D., Gray, J., Sudar, D., Lockett, S.: Segmentation of confocal microscope images of cell nuclei in thick tissue sections. J. Microsc. 193(3), 212–226 (1999)
Price, R., Tjitra, E., Guerra, C., Yeung, S., White, N., Anstey, N.: Vivax malaria: neglected and not benign. Am. J. Tropical Med. Hygience 77(6 Supplementary), 79–97 (2007)
Sarkar, D.: Detecting malaria with deep learning (2019). https://opensource.com/article/19/4/detecting-malaria-deep-learning
Shen, K.: Effect of batch size on training dynamics. Blogpost (2018). https://medium.com/mini-distill/effect-of-batch-size-on-training-dynamics-21c14f7a716e
Szegedy, C.: Deep neural networks for object detection. In: Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS), vol. 2, pp. 2553–2561 (2013)
Venkatalakshmi, B., Thilagavathi, K.: Automatic red blood cell counting using hough transform. In: 2013 IEEE Conference on Information Communication Technologies, pp. 267–271, April 2013
Wang, Sheng, Yao, Jiawen, Xu, Zheng, Huang, Junzhou: Subtype cell detection with an accelerated deep convolution neural network. In: Ourselin, Sebastien, Joskowicz, Leo, Sabuncu, Mert R., Unal, Gozde, Wells, William (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 640–648. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_74
World Health Organization: Malaria (2019). https://www.who.int/news-room/facts-in-pictures/detail/malaria
World Health Organization: New opportunities to prevent P. vivax malaria relapse (2019). https://www.who.int/malaria/news/2019/new-opportunities-to-prevent-vivax-malaria-relapse/en/
Xie, W., Noble, J.A., Zisserman, A.: Microscopy cell counting and detection with fully convolutional regression networks. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 6(3), 283–292 (2018)
Xie, Y., Xing, F., Kong, X., Su, H., Yang, L.: Beyond classification: structured regression for robust cell detection using convolutional neural network. In: Medical Image Computing and Computer-Assisted Intervention. vol. 9351, pp. 358–365, October 2015
Author information
Authors and Affiliations
Contributions
ED and AP contributed equally to the design, implementation and analysis of the research. AS and UK supervised the project and offered valuable feedback.
Corresponding author
Editor information
Editors and Affiliations
Abbreviations
- CNN
-
Convolutional Neural Network
- GT
-
Ground Truth
- HCT
-
Hough Circular Transform
- P. vivax
-
Plasmodium vivax
- ReLU
-
Rectified Linear Units
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-15-5199-4_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5198-7
Online ISBN: 978-981-15-5199-4
eBook Packages: EngineeringEngineering (R0)