Titlebook: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016; 19th International C Sebastien Ourselin,Leo Joskowicz,William Wel

Eeg332

Memory Efficient LDDMM for Lung CT,s in memory consumption and runtime are demonstrated for registration of lung CT images. State-of-the-art accuracy is shown for the challenging DIR-Lab chronic obstructive pulmonary disease (COPD) lung CT data sets obtaining a mean landmark distance after registration of 1.03?mm and the best average results so far.

揮舞

Inertial Demons: A Momentum-Based Diffeomorphic Registration Framework, a diffeomorphic registration. Our method outperforms all the analysed demons approaches in terms of speed and accuracy. Furthermore, this improvement is not limited to the demons algorithm, but applicable in most typical deformable registration algorithms.

reperfusion

Diffeomorphic Density Registration in Thoracic Computed Tomography,lty on local tissue compressibility. This algorithm appropriately models highly compressible areas of the body (such as the lungs) and incompressible areas (such as surrounding soft tissue and bones).

Cardioplegia

Pseudoephedrine

Measured

Virtues

Accuracy Estimation for Medical Image Registration Using Regression Forests,e classes (correct, poor and wrong registration) is 93.4?%, comparing favorably to a competing method. In conclusion, a method was proposed that for the first time shows the feasibility of automatic registration assessment by means of regression, and promising results were obtained.

Hiatus

gruelling

A Deep Metric for Multimodal Registration,tric is validated on intersubject deformable registration on a dataset different from the one used for training, demonstrating good generalization. In this task, we outperform mutual information by a significant margin.

cornucopia

Deformation Estimation with Automatic Sliding Boundary Computation,forced by disallowing separation or overlap between regions. Optimization alternates between discrete segmentation estimation and continuous deformation estimation. We demonstrate our method on chest 4DCT data showing sliding motion of the lungs against the thoracic cage during breathing.