基于深度学习的活体细胞有丝分裂检测方法

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.