一、影象質量和偽影（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影象超分，GAN和密集連線網路。

二、影象重建方法（Image Reconstruction Methods）

Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents P1 pp277 標準切面搜尋，深度強化學習

Real Time RNN Based 3D Ultrasound Scan Adequacy for Developmental Dysplasia of the Hip P1 pp365 超聲髖關節

三、醫學影像中的機器學習（Machine Learning in Medical Imaging）

Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks P1 pp421 擠壓-啟用網路SENet的應用

Fast Multiple Landmark Localisation Using a Patch-Based Iterative Network P1 pp563 快速多關鍵點定位

四、醫學影像中的統計分析（Statistical Analysis for Medical Imaging）

無

五、影象配準方法（Image Registration Methods）

無

六、光學和組織學應用：光學成像應用（Optical and Histology Applications: Optical Imaging Applications）

Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks P2 pp3 例項分割，Cosine嵌入，迴圈沙漏網路RHN

七、光學和組織學應用：組織學應用（Optical and Histology Applications: Histology Applications）

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

七、光學和組織學應用：顯微鏡學應用（Optical and Histology Applications: Microscopy Applications）

無

八、光學和組織學應用：光學相干斷層攝影和其它光學成像應用（Optical and Histology Applications: Optical Coherence Tomography and Other Optical Imaging Applications）

無

九、心臟，肺部和腹部應用：心臟成像應用（Cardiac，Chest and Abdominal Applications: Cardiac Imaging Applications）

Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio P2 pp544 無監督域適配

十、心臟，肺部和腹部應用：結直腸，腎臟和肝臟成像應用（Cardiac，Chest and Abdominal Applications: Colorectal, Kidney and Liver Imaging Applications）

無

十一、心臟，肺部和腹部應用：肺部成像應用（Cardiac，Chest and Abdominal Applications: Lung Imaging Applications）

Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation P2 pp777 對抗域適配

十二、心臟，肺部和腹部應用：乳腺成像應用（Cardiac，Chest and Abdominal Applications: Breast Imaging Applications）

十三、心臟，肺部和腹部應用：其他腹部應用（Cardiac，Chest and Abdominal Applications: Other Abdominal Applications）

Direct Automated Quantitative Measurement of Spine via Cascade Amplifier Regression Network P2 pp940 級聯放大器迴歸網路

十四、擴散張量成像和功能MRI：擴散張量成像（Diffusion Tensor Imaing and Funtional MRI: Diffusion Tensor Imaging）

十五、擴散張量成像和功能MRI：擴散加權成像（Diffusion Tensor Imaing and Funtional MRI: Diffusion Weighted Imaging）

十六、擴散張量成像和功能MRI：功能MRI（Diffusion Tensor Imaing and Funtional MRI: Funtional MRI）

十七、擴散張量成像和功能MRI：人類連線（Diffusion Tensor Imaing and Funtional MRI: Human Connectome）

十八、神經成像和腦部分割方法：腦部分割方法（Neuroimaging and Brain Segmentation Mehtods: Brain Segmentation Methods）

這一節，文章很多，關於腦部分割和腫瘤分割。

Semi-supervised Learning for Segmentation Under Semantic Constraint P3 pp595

3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes P3 pp612 指數對數損失

十九、計算機輔助介入：影象引導介入和手術（Computer Assisted Interventions: Image Guided Interventions and Surgery）

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

Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging P4 pp151

二十、計算機輔助介入：手術規劃，模擬模擬和工作流分析（Computer Assisted Interventions: Surgical Planning, Simulation and Work Flow Analysis）

二十一、計算機輔助介入：視覺化和增強現實（Computer Assisted Interventions: Visualization and Augmented Reality）

二十二、影象分割方法：通用分割方法，測量和應用（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、影象分割方法：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、影象分割方法：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 P4 pp497

二十五、影象分割方法：心臟分割方法（Image Segmentation Methods: Cardiac Segmentation Methods）

Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for Whole Heart Segmentation from 3D MR Images P4 569

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

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

Btrfly Net: Vertebrae Labelling with Energy-Based Adversarial Learning of Local Spine Prior P4 pp649

二十七、影象分割方法：其它分割應用（Image Segmentation Methods: Other Segmentation Applications）

Deep Reinforcement Learning for Vessel Centerline Tracing in Multi-modality 3D Volumes P4 pp755

分割任務還是被廣泛研究。另外，MR功能成像方面，我還沒有關注過。