Research on automatic classification of bone marrow cells based on microscopic hyperspectral imaging technology and deep learning
10.3760/cma.j.cn114452-20240801-00423
- VernacularTitle:基于显微高光谱成像技术和深度学习的骨髓细胞自动分类研究
- Author:
Shaomei LIU
1
;
Chi WANG
;
Yuling PAN
;
Gaixia LIU
;
Yingjiao SHA
;
Lei LIN
;
Jian DU
;
Zhoufeng ZHANG
;
Mianyang LI
Author Information
1. 解放军总医院第一医学中心检验科，北京市100853
- Publication Type:Journal Article
- Keywords:
Microspectro-imaging technology;
Deep learning;
Artificial neural network;
Cytomorphology
- From:
Chinese Journal of Laboratory Medicine
2025;48(5):616-622
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish an automatic classification approach for bone marrow cells based on microscopic hyperspectral imaging and three-dimensional spectral convolutional neural network (Spec-CNN).Methods:The research type is establishment of methodology. The study included 306 newly diagnosed patients' bone marrow smears under Wright's staining from the Department of Hematology of the First Medical Center of the PLA General Hospital from November 1st, 2013 to April 30th, 2024. The high-spectrum data and 4k image data of bone marrow cells were simultaneously collected using a microscopic hyperspectral-4k optical path integrated imaging system (with a spectral resolution of 400—1 000 nm). The high-spectrum data was used for model training, while the 4k image data recognized by morphologists was only used as a reference for labeling the high-spectrum data. The high-spectrum data set was divided into training set, validation set and test set in a ratio of 14∶6∶5. The training set and validation set were used to train and fine-tune the Spec-CNN model, and the test set was used to evaluate the model performance. The sensitivity, specificity ,accuracy ,and Kappa coefficient were calculated for comparing the manual annotation results as gold standard with the intelligent identification results of the Spec-CNN model. Five non-data set samples were used for external validation.Results:The acquired hyperspectral data and 4k imaging dataset comprised of 32 categories and 64 800 bone marrow cells. In the test set, the Spec-CNN model demonstrated weighted-average indicators on classification metrics across 32 cell types: sensitivity 87.79%, specificity 99.31%, and accuracy 98.78%, and Kappa coefficient 0.869. For external validation, the mean correct identification rate of bone marrow cells reached 83.28%.Conclusion:We successfully established an automatic classification method of bone marrow cells based on microscopic hyperspectral imaging and three-dimensional Spec-CNN. This method has a good automatic classification ability for 32 types of bone marrow nucleated cells, which has a certain auxiliary effect on improving the diagnosis efficiency of blood diseases for bone marrow morphologists.
