基于数据增强的CT图像病灶检测方法

基于医疗影像的辅助诊断技术正处于快速发展阶段,但是受医学影像数据量的制约,使得基于深度学习的建模方法无法向更复杂的模型进行探索.本文从医学CT影像数据增强方法出发,概述了医疗影像病灶图像的成像特点,针对病灶检测及分割任务对现有方法进行了归类总结,并阐述了当前医学影像检测和分割的难点.分别从病灶检测相关技术、影像数据增强方法、基于生成对抗网络(Generative Adversarial Network,GAN)的病灶检测方法等方面进行了总结.最后,针对医学领域内基于深度学习的数据增强方法:标准GAN、pix2pixGAN、CycleGAN模型进行了对比分析,并展望未来医学影像分析领域的发展趋势.

Lesion Detection Method for CT Images Based on Data Augmentation

At present, the computer aided diagnosis technology based on medical imaging is at a stage of rapid development, but limited by the medical imaging data size, the modeling method based on deep learning cannot explore more complex models. Starting from the data augmentation method for medical CT images, this article summarizes the imaging characteristics of medical lesion images, classifies the existing methods for lesion detection and segmentation tasks, and expounds on the current difficulties in medical image detection and segmentation. It summarizes the related technologies of medical lesion detection, the data augmentation methods, and the lesion detection methods based on the Generative Adversarial Network (GAN). Finally, the data augmentation methods based on deep learning in the medical field, including GAN, pix2pixGAN, and CycleGAN models, are comparatively analyzed, and the future development trend of medical image analysis is prospected.