2025
doi: 10.12720/jait.16.5.648-654
Automatic Identification of CTC in Fluorescence Microscopy Images Based on A Lightweight Hybrid Network Model
,
Kouki Tsuji1
,
Kazue Yoneda2,3 ,
Tohru Kamiya1,*
- 1Department of Mechanical and Control Engineering, Kyushu Institute of Technology, 1-1, Sensui, Tobata, Kitakyushu 804-8550, Japan
- 2Department of Thoracic Oncology, School of Medicine, Hyogo Medical University, 1-1, Mukogawa-cho, Nishinomiya, Hyogo 663-8501, Japan
- 3Second Department of Surgery (Chest Surgery), University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi, Kitakyushu 807-8555, Japan
- Manuscript receivedDecember 19, 2024
- revisedJanuary 23, 2025
- acceptedMarch 6, 2025
- publishedMay 9, 2025
Abstract
Circulating Tumor Cells (CTC) are expected to be a useful biomarker for cancer metastasis. CTC analysis can be used to assess cancer status and the therapeutic effects of anticancer drugs. Pathologists analyze blood samples from images taken with a fluorescence microscope. However, the number of CTCs in blood is very small, which is a time-consuming task. In this paper, we propose an automatic CTC identification method from fluorescence microscopy images. In the proposed method, we detect cell regions using a selective enhancement filter and blob analysis. Then, we identify the CTC using a SqueezeNet-based Convolutional Neural Network (CNN) model with Spatial Pyramid Pooling (SPP). We apply the proposed method to 5040 microscopy images and evaluate its effectiveness. The experimental results show that the proposed method has a true positive rate of 97.30%, a false positive rate of 2.069%, and an Area Under the Curve (AUC) of 0.991.
Keywords
- Circulating Tumor Cells (CTC)
- fluorescence microscopy images
- SqueezeNet
- Spatial Pyramid Pooling (SPP)
How to Cite
Kazuki Nakamichi, Kouki Tsuji, Kazue Yoneda, and Tohru Kamiya, "Automatic Identification of CTC in Fluorescence Microscopy Images Based on A Lightweight Hybrid Network Model," Journal of Advances in Information Technology, vol. 16, no. 5, pp. 648-654, 2025. https://doi.org/10.12720/jait.16.5.648-654
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
