Anisotropic Deep Learning Multi-planar Automatic Prostate Segmentation

Author:Tabea Riepe  Matin Hosseinzadeh  Patrick Brand  Henkjan Huisman  

Institution:Radboudumc  

Session Type:Digital Poster  

Session Live Q&A Date:Digital Poster (All Week)  

Topic:ML: Post Processing, Analysis, & Applications  

Session Name:Machine Learning: Image Segmentation  

Program Number:3518  

Room Live Q&A Session:

Automated Quantification of Lung Cysts at 0.55T MRI with Image Synthesis from CT using Deep Learning

Author:Ipshita Bhattacharya  Marcus Chen  Joel Moss  Adrienne Campbell-Washburn  Hui Xue  

Institution:National Institutes of Health  

Session Type:Digital Poster  

Session Live Q&A Date:Digital Poster (All Week)  

Topic:ML: Post Processing, Analysis, & Applications  

Session Name:Machine Learning: Image Segmentation  

Program Number:3513  

Room Live Q&A Session:

Automated Segmentation for Myocardial Tissue Phase Mapping Images using Deep Learning

Automatic segmentation of dentate nuclei for microstructure assessment: application to temporal lobe epilepsy patients

A Cascaded 3D CNN Approach for Thalamic Nuclei Segmentation

A deep neural network with convolutional LSTM for brain tumor segmentation in multi-contrast volumetric MRI

Author:Namho Jeong  Byungjai Kim  Jongyeon Lee  HyunWook Park  

Institution:Korea Advanced Institute of Science and Technology  

Session Type:Digital Poster  

Session Live Q&A Date:Digital Poster (All Week)  

Topic:ML: Post Processing, Analysis, & Applications  

Session Name:Machine Learning: Image Segmentation  

Program Number:3510  

Room Live Q&A Session:

Multi-Contrast Hippocampal Subfield Segmentation for Ultra-High Field 7T MRI Data using Deep Learning

Multi-scale Entity Encoder-decoder Network Learning for Stroke Lesion Segmentation

Multi-Task Learning: Segmentation as an auxiliary task for Survival Prediction of cancer using Deep Learning

Region of Interest Localization in Large 3D Medical Volumes by Deep Voting

Segmenting Brain Tumor Lesion from 3D FLAIR MR Images using Support Vector Machine approach

Simulated CMR images can improve the performance and generalization capability of deep learning-based segmentation algorithms