Medical Computer Vision Algorithms for Big Data 1st Edition by Bjoern Menze, Georg Langs, Albert Montillo, Michael Kelm, Henning Muller, Shaoting Zhang, Weidong, Cai, Dimitris Metaxas ISBN 3319139715 9783319139715

Workshop Overview

• Overview of the 2014 Workshop on Medical Computer Vision—Algorithms for Big Data (MCV 2014)

Segmentation of Big Medical Data

• Joint Segmentation and Registration for Infant Brain Images

• LINKS: Learning-Based Multi-source IntegratioN FrameworK for Segmentation of Infant Brain Images

• Pectoralis Muscle Segmentation on CT Images Based on Bayesian Graph Cuts with a Subject-Tailored Atlas

Advanced Feature Extraction

• Learning Features for Tissue Classification with the Classification Restricted Boltzmann Machine

• Dementia-Related Features in Longitudinal MRI: Tracking Keypoints over Time

• Object Classification in an Ultrasound Video Using LP-SIFT Features

• Unsupervised Pre-training Across Image Domains Improves Lung Tissue Classification

Multi-atlas and Beyond

• Atlas-Guided Multi-channel Forest Learning for Human Brain Labeling

• Fast Multiatlas Selection Using Composition of Transformations for Radiation Therapy Planning

• Classifier-Based Multi-atlas Label Propagation with Test-Specific Atlas Weighting for Correspondence-Free Scenarios

Translational Medical Computer Vision

• CT Prostate Deformable Segmentation by Boundary Regression

• Precise Lumen Segmentation in Coronary Computed Tomography Angiography

• Confidence-Based Training for Clinical Data Uncertainty in Image-Based Prediction of Cardiac Ablation Targets

VISCERAL Session

• Rule-Based Ventral Cavity Multi-organ Automatic Segmentation in CT Scans

• Multi-atlas Segmentation and Landmark Localization in Images with Large Field of View

• Automatic Liver Segmentation Using Statistical Prior Models and Free-form Deformation

• Hierarchic Multi–atlas Based Segmentation for Anatomical Structures: Evaluation in the VISCERAL Anatomy Benchmarks

• Automatic 3D Multiorgan Segmentation via Clustering and Graph Cut Using Spatial Relations and Hierarchical Models