Electrical Engineering Department, Sharif University of Technology, Tehran, Iran.
Computer Engineering Department, Sharif University of Technology, Tehran, Iran.
J Bioinform Comput Biol. 2022 Jun;20(3):2250011. doi: 10.1142/S0219720022500111. Epub 2022 Jul 7.
Karyotype is a genetic test that is used for detection of chromosomal defects. In a karyotype test, an image is captured from chromosomes during the cell division. The captured images are then analyzed by cytogeneticists in order to detect possible chromosomal defects. In this paper, we have proposed an automated pipeline for analysis of karyotype images. There are three main steps for karyotype image analysis: image enhancement, image segmentation and chromosome classification. In this paper, we have proposed a novel chromosome segmentation algorithm to decompose overlapped chromosomes. We have also proposed a CNN-based classifier which outperforms all the existing classifiers. Our classifier is trained by a dataset of about 1,62,000 human chromosome images. We also introduced a novel post-processing algorithm which improves the classification results. The success rate of our segmentation algorithm is 95%. In addition, our experimental results show that the accuracy of our classifier for human chromosomes is 92.63% and our novel post-processing algorithm increases the classification results to 94%.
核型是一种用于检测染色体缺陷的遗传检测。在核型检测中，在细胞分裂过程中从染色体捕获图像。然后由细胞遗传学家分析捕获的图像，以检测可能的染色体缺陷。在本文中，我们提出了一种用于分析核型图像的自动化流水线。核型图像分析有三个主要步骤：图像增强、图像分割和染色体分类。在本文中，我们提出了一种新颖的染色体分割算法，用于分解重叠的染色体。我们还提出了一种基于 CNN 的分类器，其性能优于所有现有的分类器。我们的分类器是通过大约 162,000 个人类染色体图像的数据集进行训练的。我们还引入了一种新颖的后处理算法，该算法可以提高分类结果。我们的分割算法的成功率为 95%。此外，我们的实验结果表明，我们的人类染色体分类器的准确率为 92.63%，我们的新型后处理算法可以将分类结果提高到 94%。
