基于可解释卷积神经网络的胚胎血管新生时间模式研究

doi:10.3969/j.issn.0258-8021.2020.05.002

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Abstract Understanding temporal patterns of vascular network angiogenesis facilitates the study of biological development mechanisms and the pathophysiology of tumors. In this paper,an interpretable convolutional neural network (CNN) was proposed to study the temporal pattern of angiogenesis of the chicken vitelline vascular networks. We constructed a model for classifying the vascular images of chicken vitelline at 3 days post-fertilization (3dpf) and 4 days post-fertilization (4dpf) based on CNN,explained the classification results using gradient-weighted class activation mapping (Grad-CAM),and analyzed the angiogenic pattern from 3dpf to 4dpf based on the model. A total of 17 fertilized eggs were observed in the experiment. Results showed that the accuracy of the optimal model to classify 3dpf and 4dpf vascular images was 98.62%. Using the Grad-CAM technique,we found out that the main manifestation of the vascular network development from 3dpf to 4dpf was the growth of capillary networks. Between 3dpf and 4dpf,the process of angiogenesis was more intense during the first 12 hours and then tended to be stable. This study provided new approaches for the researches on angiogenesis,assisting the physiological studies including angiogenesis mechanisms,tumor growth and organ aging.

Key words： angiogenesis convolutional neural network chicken vitelline gradient-weighted class activation mapping

Received: 10 December 2019

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R318

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	Articles by authors
	Lv Xueqian
	Zhao Shenjia
	Li Peilun
	Fang Luping
	Ning Gangmin
	Pan Qing

Cite this article:

Lv Xueqian,Zhao Shenjia,Li Peilun, et al. Study of Temporal Pattern of Embryonic Angiogenesis Based on Interpretable Convolutional Neural Networks[J]. Chinese Journal of Biomedical Engineering, 2020, 39(5): 524-531.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.05.002 OR http://cjbme.csbme.org/EN/Y2020/V39/I5/524

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