Automatic Detection Method for Cancer Cell Nucleus Image Based on Deep-Learning Analysis and Color Layer Signature Analysis Algorithm

• Published in: Sensors (Basel, Switzerland) Switzerland), 2020-08, Vol.20 (16), p.4409
• Main Authors: Su, Hsing-Hao, Pan, Hung-Wei, Lu, Chuan-Pin, Chuang, Jyun-Jie, Yang, Tsan
• Format: Article
• Language: English
• Subjects: Algorithms; Apoptosis; Artificial intelligence; Cell cycle; Cell death; Cell Nucleus; Chromosomes; Colon; Colorectal cancer; computer vision; convolutional neural network; Deep Learning; Defects; Drugs; Genomes; Image Processing, Computer-Assisted; Medical imaging; Medical research; micronuclei; Microscopy; mitosis; Neoplasms - diagnosis; Neural networks; Neural Networks, Computer; Nuclei (cytology); nucleus; Researchers; Signature analysis; Stability analysis; Workflow
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20164409

Exploring strategies to treat cancer has always been an aim of medical researchers. One of the available strategies is to use targeted therapy drugs to make the chromosomes in cancer cells unstable such that cell death can be induced, and the elimination of highly proliferative cancer cells can be achieved. Studies have reported that the mitotic defects and micronuclei in cancer cells can be used as biomarkers to evaluate the instability of the chromosomes. Researchers use these two biomarkers to assess the effects of drugs on eliminating cancer cells. However, manual work is required to count the number of cells exhibiting mitotic defects and micronuclei either directly from the viewing window of a microscope or from an image, which is tedious and creates errors. Therefore, this study aims to detect cells with mitotic defects and micronuclei by applying an approach that can automatically count the targets. This approach integrates the application of a convolutional neural network for normal cell identification and the proposed color layer signature analysis (CLSA) to spot cells with mitotic defects and micronuclei. This approach provides a method for researchers to detect colon cancer cells in an accurate and time-efficient manner, thereby decreasing errors and the processing time. The following sections will illustrate the methodology and workflow design of this study, as well as explain the practicality of the experimental comparisons and the results that were used to validate the practicality of this algorithm.