Abstract
The article investigates the application of a neural network diagnosis model to histological images in order to detect plasma cells for chronic endometritis detection. A two-stage algorithm was developed for plasma cell detection. At the first stage, a CenterNet model was used to detect stromal and epithelial cells. The neural network was trained on an open dataset with histological images and further fine-tuned using an additional labeled dataset. A labeling protocol was used, and the coefficient of agreement between two experts was calculated, which turned out to be 0.81. At the second stage, using the developed algorithm based on computer vision methods, plasma cells were identified and their HSV color boundaries were calculated. For the two-stage algorithm the following quality metrics were obtained: precision = 0.70, recall = 0.43, f1-score = 0.53. The model then was modified to detect only plasma cells and trained on a dataset with histological images containing labeled plasma cells. The quality metrics of the modified detection model were obtained: precision = 0.73, recall = 0.89, f1-score = 0.8. As a result of the comparison, the modified detection model approach showed the best quality metrics. Automating the work of counting plasma cells will allow doctors to spend less time on routine activities.
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.REFERENCES
Ibragimov, A., Senotrusova, S., Markova, K., Karpulevich, E., Ivanov, A., Tyshchuk, E., and Sokolov, D., Deep semantic segmentation of angiogenesis images, Int. J. Mol. Sci., 2023, vol. 24, no. 2, p. 1102.
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q., Centernet: Keypoint triplets for object detection, Proc. IEEE/CVF Int. Conf. on Computer Vision, Seoul, 2019, pp. 6569–6578.
Naumov, A., Ushakov, E., Ivanov, A., Midiber, K., Khovanskaya, T., Konyukova, A., and Karpulevich, E., EndoNuke: Nuclei detection dataset for estrogen and progesterone stained IHC endometrium scans, Data, 2022, vol. 7, no. 6, p. 75.
Too, E.C., Yujian, L., Njuki, S., and Yingchun, L., A comparative study of fine-tuning deep learning models for plant disease identification, Comput. Electron. Agric., 2019, vol. 161, pp. 272–279.
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J., UNet++: Ф nested U-Net architecture for medical image segmentation, Proc. 4th Int. Workshop on Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support DLMIA 2018, and 8th Int. Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, 2018. https://doi.org/10.1007/978-3-030-00889-5_1
He, K., Zhang, X., Ren, S., and Sun, J., Deep residual learning for image recognition, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, 2016, pp. 770–778.
Gokcesu, K. and Gokcesu, H., Generalized huber loss for robust learning and its efficient minimization for a robust statistics, 2021. arXiv preprint arXiv:2108.12627
Bankhead, P., Loughrey, M.B., Fernández, J.A., Dombrowski, Y., McArt, D.G., Dunne, P.D., and Hamilton, P.W., QuPath: open source software for digital pathology image analysis, Sci. Rep., 2017, vol. 7, no. 1, pp. 1–7.
Müller, D., Soto-Rey, I., and Kramer, F., Towards a guideline for evaluation metrics in medical image segmentation, BMC Res. Notes, 2022, vol. 15, no. 1, pp. 1–8.
Shah, A., Bangash, J. I., Khan, A. W., Ahmed, I., Khan, A., Khan, A., and Khan, A., Comparative analysis of median filter and its variants for removal of impulse noise from gray scale images, J. King Saud Univ.–Comput. Inf. Sci., 2022, vol. 34, no. 3, pp. 505–519.
Eilertsen, G., Mantiuk, R.K., and Unger, J., A comparative review of tone-mapping algorithms for high dynamic range video, in Proc. Computer Graphics Forum, May 2017, vol. 36, no. 2, pp. 565–592.
Bora, D.J., Gupta, A.K., and Khan, F.A., Comparing the performance of L* A* B* and HSV color spaces with respect to color image segmentation, 2015. arXiv:1506.01472
Amherd, F. and Rodriguez, E., Heatmap-based object detection and tracking with a fully convolutional neural network, 2021. arXiv:2101.03541
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., and Chintala, S., Pytorch: An imperative style, high-performance deep learning library, Proc. 33rd Int. Conf. on Neural Information Processing Systems NIPS’19, Vancouver, 2019.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., and Fei-Fei, L., Imagenet large scale visual recognition challenge, Int. J. Comput. Vision, 2015, vol. 115, pp. 211–252.
Funding
This work was supported by the Ministry of Science and Higher Education of the Russian Federation, agreement no. 075-15-2022-294 dated 15 April 2022.
This work was supported by the State assignment no. 223013000171-4.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
The authors declare that they have no conflicts of interest.
Additional information
Publisher’s Note.
Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Makarchuk, A., Asaturova, A., Ushakov, E. et al. Artificial Intelligence (AI) Solution for Plasma Cells Detection. Program Comput Soft 49, 873–880 (2023). https://doi.org/10.1134/S0361768823080121
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0361768823080121