题目：Automatic Brain Metastasis Detection by Convolutional Neural Network
报告人：Prof. Jyh-Cheng Chen（陈志成 教授）
National Yang-Ming University, Taipei, Taiwa（台湾阳明大学）
时间：2019年5月15日（周三）, 上午10:00
地点：上海理工大学医疗器械与食品学院，综合楼C区3楼 302会议室
报告人简介：
陈志成，博士，台湾阳明大学教授，生医影像与放射系主任。1983年获台湾中央大学 (又称中国南京大学) 物理学学士学位, 1988年和1995年获得美国亚利桑那州大学物理学硕士学位和光学科学博士学位。1995年在台湾工业技术研究所光电和系统实验室工作。1996年在台湾阳明大学医学技术系放射科学与技术系工作，任放射科学研究所副教授。2005年任台湾大学生物医学成像和放射科学 (BIRS) 教授, 从事分子成像物理和仪器领域的研究。目前的研究领域是图像处理、分析和重建。包括使用micro PET、micro SPECT、micro CT进行图像重建、处理和分析, 用于动物分子成像研究。自主设计制作了micro CT、FMTT和 PET/CT小动物成像系统。共发表学术论文100余篇, 多次撰写专业著作章节，并拥有多项专利。获得了台湾阳明大学教师学术卓越奖等研究奖项。他是 IEEE、SNM 和 FASMI 的会士，担任《国际生物医学成像杂志》的编委委员、《计算机化医学成像和图形》特刊的特邀编辑、超过15份国际科学期刊的评审员，以及ANMMI和JRS的联合编辑。
报告内容：
Objective：Brain metastasis is the most common cerebral neoplasm in adults. Post-contrast MR has the best detectability to detect brain metastasis among all imaging modalities, yet recognition of the pathology is cognitive demanding even for experts. The purpose of our study was to use convolutional neural network (CNN) algorithm to facilitate automatic detection of metastasis in MR images. Materials and methods: Fifty metastatic patients with post-contrast axial T1 weighted MR of 3-mm slices to cover the whole brain were used as the whole dataset. Metastasis lesions were manually annotated by an experienced radiologist and served as the gold standard. Two-dimensional slices were cropped and normalized, and then were transferred to CNN structure for training. Various internal structures were attempted with different filter size. We tried some parameters such as 28 layers and 24 batch size. We chosed the exponential linear unit as activation function. The initial learning rate was 0.05, and decayed by 0.6 every 200 steps. The cost function consists of mean square error and Dice coefficient. The performance was evaluated with Dice coefficient and area under ROC curve (AUC). All the experiments were performed using Tensorflow in Python 3.5. Results: The CNN model with chosen parameters achieved a Dice score of 0.68 and AUC score of 0.84 on our testing data. Conclusion: CNN effectively facilitates the recognition of metastatic lesions in post-contrast MR images. Further evaluation with more datasets obtained by different protocols and institutions may improve its detection accuracy.