这篇博客是关于2018年医学影像计算和计算机辅助干预国际会议（MICCAI）的阅读笔记，涵盖了图像质量和伪影、图像重建、机器学习在医学影像中的应用、统计分析、图像配准、光学和组织学成像、心脏和腹部成像等多个领域。文中提到了使用深度学习方法如GAN和RNN在MRI超分辨率、超声成像、图像重建和配准中的应用，并探讨了在各种医学成像中如心脏、肺部、腹部、乳腺等的分割和检测技术。
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http://blog.youkuaiyun.com/dgyuanshaofeng/article/details/82902023
一、图像质量和伪影（Image Quality and Artefacts）
Efficient and Accurate MRI Super-Resolution Using a Generative Adversarial Network and 3D Multi-level Densely Connected Network
P1 pp91
MRI图像超分，GAN和密集连接网络。
High Frame-Rate Cardiac Ultrasound Imaging with Deep Learning
Part1, pp. 126-134
深度学习加速超声成像
二、图像重建方法（Image Reconstruction Methods）
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
P1 pp277
标准切面搜索，深度强化学习
Real Time RNN Based 3D Ultrasound Scan Adequacy for Developmental Dysplasia of the Hip
Part 1, pp. 365-373
超声髋关节
Direct Reconstruction of Ultrasound Elastography Using an End-to-End Deep Neural Network
Part 1, pp. 374-382
3D Fetal Skull Reconstruction from 2DUS via Deep Conditional Generative Networks
Part 1, pp. 383-391
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
Part 1, pp. 392-00
三维胎儿超声标准切面检测
三、医学影像中的机器学习（Machine Learning in Medical Imaging）
Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks
P1 pp421
挤压-激活网络SENet的应用
Fast Multiple Landmark Localisation Using a Patch-Based Iterative Network
Part 1 pp. 563-571
快速多关键点定位
四、医学影像中的统计分析（Statistical Analysis for Medical Imaging）
无
五、图像配准方法（Image Registration Methods）
Adversarial Deformation Regularization for Training Image Registration Neural Networks
Part 1 pp. 774-782
Initialize Globally Before Acting Locally: Enabling Landmark-Free 3D US to MRI Registration
Part 1 pp. 827-835</
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