「MICCAI 2018」Reading Notes

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http://blog.csdn.net/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

Multi-task SonoEyeNet: Detection of Fetal Standardized Planes Assisted by Generated Sonographer Attention Maps
Part 1 pp. 871-879

六、光学和组织学应用：光学成像应用（Optical and Histology Applications: Optical Imaging Applications）

Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks
P2 pp3
实例分割，Cosine嵌入，循环沙漏网络RHN

A Pixel-Wise Distance Regression Approach for Joint Retinal Optical Disc and Fovea Detection
P2 pp39

七、光学和组织学应用：组织学应用（Optical and Histology Applications: Histology Applications）

A Deep Model with Shape-Preserving Loss for Gland Instance Segmentation
P2 pp138

七、光学和组织学应用：显微镜学应用（Optical and Histology Applications: Microscopy Applications）

无

八、光学和组织学应用：光学相干断层摄影和其它光学成像应用（Optical and Histology Applications: Optical Coherence Tomography and Other Optical Imaging Applications）

无

九、心脏，肺部和腹部应用：心脏成像应用（Cardiac，Chest and Abdominal Applications: Cardiac Imaging Applications）

More Knowledge Is Better: Cross-Modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring
Part 2, pp. 535-543

Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
P2 pp544
无监督域适配

十、心脏，肺部和腹部应用：结直肠，肾脏和肝脏成像应用（Cardiac，Chest and Abdominal Applications: Colorectal, Kidney and Liver Imaging Applications）

Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images
Part 2 pp. 711-719

十一、心脏，肺部和腹部应用：肺部成像应用（Cardiac，Chest and Abdominal Applications: Lung Imaging Applications）

Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation
P2 pp777
对抗域适配

十二、心脏，肺部和腹部应用：乳腺成像应用（Cardiac，Chest and Abdominal Applications: Breast Imaging Applications）

Integrate Domain Knowledge in Training CNN for Ultrasonography Breast Cancer Diagnosis
Part 2, pp. 868-875

十三、心脏，肺部和腹部应用：其他腹部应用（Cardiac，Chest and Abdominal Applications: Other Abdominal Applications）

AutoDVT: Joint Real-Time Classification for Vein Compressibility Analysis in Deep Vein Thrombosis Ultrasound Diagnostics
Part 2, pp. 905-912

Automatic Lacunae Localization in Placental Ultrasound Images via Layer Aggregation
Part 2, pp. 921-929

Direct Automated Quantitative Measurement of Spine via Cascade Amplifier Regression Network
Part 2, pp. 940-948
级联放大器回归网络

十四、扩散张量成像和功能MRI：扩散张量成像（Diffusion Tensor Imaing and Funtional MRI: Diffusion Tensor Imaging）

无

十五、扩散张量成像和功能MRI：扩散加权成像（Diffusion Tensor Imaing and Funtional MRI: Diffusion Weighted Imaging）

无

十六、扩散张量成像和功能MRI：功能MRI（Diffusion Tensor Imaing and Funtional MRI: Funtional MRI）

无。

十七、扩散张量成像和功能MRI：人类连接（Diffusion Tensor Imaing and Funtional MRI: Human Connectome）

无。

神经成像和脑部分割方法：神经成像（Neuroimaging and Brain Segmentation Mehtods: Neuroimaging）

Dilatation of Lateral Ventricles with Brain Volumes in Infants with 3D Transfontanelle US
Part 3, pp. 557-565

十八、神经成像和脑部分割方法：脑部分割方法（Neuroimaging and Brain Segmentation Mehtods: Brain Segmentation Methods）

这一节，文章很多，关于脑部分割和肿瘤分割。

Semi-supervised Learning for Segmentation Under Semantic Constraint
P3 pp595

3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
P3 pp612
指数对数损失

十九、计算机辅助介入：图像引导介入和手术（Computer Assisted Interventions: Image Guided Interventions and Surgery）

Learning from Noisy Label Statistics: Detecting High Grade Prostate Cancer in Ultrasound Guided Biopsy
Part 4, pp. 21-29

A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
Part 4, pp. 30-38

X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery
P4 pp55

Simultaneous Segmentation and Classificatin of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN
Part 4, pp. 134-142

Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging
Part 4, pp. 151-158

二十、计算机辅助介入：手术规划，模拟仿真和工作流分析（Computer Assisted Interventions: Surgical Planning, Simulation and Work Flow Analysis）

无。

二十一、计算机辅助介入：可视化和增强现实（Computer Assisted Interventions: Visualization and Augmented Reality）

Framework for Fusion of Data- and Model-Based Approaches for Ultrasound Simulation
Part 4, pp. 332-339

二十二、图像分割方法：通用分割方法，测量和应用（Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications）

MS-Net: Mixed-Supervision Fully-Convolutional Networks for Full-Resolution Segmentation
P4 pp379

二十三、图像分割方法：多器官分割（Image Segmentation Methods: Multi-organ Segmentation）

A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation
P4 pp417

3D U-JAPA-Net: Mixture of Convolutional Networks for Abdominal Multi-organ CT Segmentation
P4 pp426

二十四、图像分割方法：腹部分割方法（Image Segmentation Methods: Abdominal Segmentation Methods）

Segmentation of Renal Structures for Image-Guided Surgery
P4 pp454

Generalizing Deep Models for Ultrasound Image Segmentation
Part 4, pp. 497-505

二十五、图像分割方法：心脏分割方法（Image Segmentation Methods: Cardiac Segmentation Methods）

Deep Attentional Features for Prostate Segmentation in Ultrasound
Part 4, pp. 523-530

Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for Whole Heart Segmentation from 3D MR Images
P4 pp569

Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations
P4 pp586

二十六、图像分割方法：胸部，肺部和脊椎分割（Image Segmentation Methods: Chest，Lung and Spine Segmentation）

Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound
Part 4, pp. 641-648

Btrfly Net: Vertebrae Labelling with Energy-Based Adversarial Learning of Local Spine Prior
Part 4, pp. 649-657

二十七、图像分割方法：其它分割应用（Image Segmentation Methods: Other Segmentation Applications）

Fast Vessel Segmentation and Tracking in Ultra High-Frequency Ultrasound Images
Part 4, pp. 746-754

Deep Reinforcement Learning for Vessel Centerline Tracing in Multi-modality 3D Volumes
Part 4, pp. 755-763

分割任务还是被广泛研究。另外，MR功能成像方面，我还没有关注过。