基于深度学习的活体细胞有丝分裂检测方法

Baosheng Ke, Ying Li, Zhenbo Ren, Jianglei Di, Jianlin Zhao

科研成果: 期刊稿件文章同行评审

3 引用 (Scopus)

摘要

Owing to the spatiotemporal randomness of mitosis, the automatic identification and accurate location of mitosis in living cells are challenging tasks for researchers. Herein, a deep learning-based detection method was proposed to automatically identify and locate mitosis in living cells. Here, we built a deep neural network called DetectNet by improving the backbone network of YOLOv3 and introducing an attention mechanism. Under the condition of bright-field microscopic imaging, multiscale images of living cells were acquired and then a dataset was constructed to train the network. The trained network DetectNet was compared with multiple object detection algorithms, and its effectiveness was verified. Experimental results show that aiming at the bright-field microscopic images, DetectNet can directly identify and locate mitosis from the multiscale live cell images with a large field, achieving a higher detection accuracy and faster detection speed compared with other multiple object detection algorithms. Thus, DetectNet shows a great potential application value in the fields of biology and medicine.

投稿的翻译标题Deep Learning-Based Detection Method for Mitosis in Living Cells
源语言繁体中文
文章编号1511001
期刊Guangxue Xuebao/Acta Optica Sinica
41
15
DOI
出版状态已出版 - 10 8月 2021

关键词

  • Bright field microscopic imaging
  • Deep learning
  • Imaging systems
  • Living cell
  • Mitosis
  • Object detection algorithm

指纹

探究 '基于深度学习的活体细胞有丝分裂检测方法' 的科研主题。它们共同构成独一无二的指纹。

引用此