医学影像诊断教学中的教学手段和认知工具滞后于信息技术的发展，难以满足医学影像诊断教学中的智能化和个性化需求，不利于医学影像诊断学人才的培养。本项目着眼于人体内发病率最高的器官-肺，从教学内容可视化和教学手段智能化两个教学需求出发，进行基于CT影像的计算机辅助肺病诊断教学关键技术研究。主要研究内容有：利用双通道去噪增强技术对CT图像进行细节强化；融合多维度多尺度特征和上下文监督机制的模糊森林分类技术辅助CT图像个性化呈现；从CT图像提取密集、均匀三维点云，重建肺部组织空间关系；建立深度学习神经网络对病灶进行准确分类；以及融合特征提取，边缘检测等技术进行医学特征智能标注和学生作答智能评测等。通过基于CT影像的计算机辅助肺病诊断教学关键技术研究，增强学习者的学习体验，解决教师教学困难，有效辅助医学影像诊断教学活动开展，进而促进人工智能技术与医学教学的深度融合，推动医学教育信息化发展。

Teaching methods and cognitive tools in medical imaging diagnosis teaching lag behind the development of information technology, which is difficult to meet the intelligent and personalized needs in medical imaging diagnosis teaching, and is not conducive to the cultivation of medical imaging diagnosis talents. This project focuses on the organ with the highest incidence in the human body, lung, starting from two teaching requirements of teaching content visualization and teaching method intelligence, conducts research on the key techniques of computer-aided lung disease diagnosis teaching based on CT images. The main research contents include details of CT image enhancement using a double-channel denoising and enhancement technique, personalized exhibition of CT images with a fuzzy forest classification technique combining multi-dimensional and multi-scale features and context monitoring mechanism, reconstruction of the spatial relationship between lung tissues utilizing dense and uniform three-dimensional point clouds extracted from CT images, accurate lesion classification of lung diseases based on a deep learning neural network, medical feature intelligent labeling and students answer intelligent evaluation fusing feature extraction, edge detection, and other techniques. The research of the key techniques of computer-aided pulmonary disease diagnosis teaching based on CT images can enhance the learning experience of learners, solve the teaching difficulties of teachers and assist the teaching activities in medical image diagnosis, so as to promote the deep integration of artificial intelligence technology and medical teaching and promote the development of medical education informatization.