Automated Counting of Cancer Cells by Ensembling Deep Features

Cells

Kazuhiro Murakami¹  Pingzhao Hu²  Qian Liu²  Anna Junker³ 

[1] Cancer Research Institute, Kanazawa University, Kanazawa 920 1192, Japan;Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB R3E 0J9, Canada;European Institute of Molecular Imaging, University of Münster, D-48149 Münster, Germany;

关键词: microscopic imaging;    automatic cell counting;    deep learning;    transfer learning;    autoencoder;    ensembling feature;   

DOI  :  10.3390/cells8091019

来源: DOAJ

【 摘 要 】

High-content and high-throughput digital microscopes have generated large image sets in biological experiments and clinical practice. Automatic image analysis techniques, such as cell counting, are in high demand. Here, cell counting was treated as a regression problem using image features (phenotypes) extracted by deep learning models. Three deep convolutional neural network models were developed to regress image features to their cell counts in an end-to-end way. Theoretically, ensembling imaging phenotypes should have better representative ability than a single type of imaging phenotype. We implemented this idea by integrating two types of imaging phenotypes (dot density map and foreground mask) extracted by two autoencoders and regressing the ensembled imaging phenotypes to cell counts afterwards. Two publicly available datasets with synthetic microscopic images were used to train and test the proposed models. Root mean square error, mean absolute error, mean absolute percent error, and Pearson correlation were applied to evaluate the models’ performance. The well-trained models were also applied to predict the cancer cell counts of real microscopic images acquired in a biological experiment to evaluate the roles of two colorectal-cancer-related genes. The proposed model by ensembling deep imaging features showed better performance in terms of smaller errors and larger correlations than those based on a single type of imaging feature. Overall, all models’ predictions showed a high correlation with the true cell counts. The ensembling-based model integrated high-level imaging phenotypes to improve the estimation of cell counts from high-content and high-throughput microscopic images.

【 授权许可】

Unknown