Abstract
At present, tumor-associated macrophages (TAMs) are receiving substantial attention owing to their potential as new therapeutic targets. However, the recognition and counting of TAMs remains an open problem. The results of existing algorithms and procedures are unsatisfactory and exhibit multiple defects such as blurred edges or a long inference time. In this work, we first propose an algorithm based on simple linear iterative clustering for the automatic selection of TAMs-dense hot spots in huge whole slide imaging. Subsequently, we present an end-to-end method based on U-Net for the segmentation and counting of pleomorphic TAMs. Edge detection is incorporated into the network architecture and nuclei information is used for verification. The experimental results demonstrate that our method achieved the highest F1-score and relatively good edge segmentation accuracy with an acceptable parameter size on a constructed dataset. The average counting results of our method also exhibited a comparatively small deviation, thereby demonstrating the possibility for clinical application.
The code and dataset are available at https://github.com/Meteorsc9/Decouple-U-Net.
Z. Chen and H. Yang—These authors have contributed equally to this work.
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Acknowledgements
This work is supported by Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities PKU2022LCXQ19 and Innovative Experiment Project for Undergraduates of Peking University Health Science Center 2021-SSDC-14. We would like to thank Yan Gao and Jianyun Zhang for their work on annotating images.
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Chen, Z., Yang, H., Gao, M., Hu, X., Li, Y., Wang, L. (2022). Decouple U-Net: A Method for the Segmentation and Counting of Macrophages in Whole Slide Imaging. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13535. Springer, Cham. https://doi.org/10.1007/978-3-031-18910-4_9
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