Ten papers accepted at MICCAI 2016

Publications

The BioMedIA group has 10 papers accepted at this year’s International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI) which will be held in Athens in October.

Salim Arslan, Sarah Parisot, Daniel Rueckert
Boundary Mapping through Manifold Learning for Connectivity-Based Cortical Parcellation

Christian Ledig, Sebastian Kaltwang, Antti Tolonen, Juha Koikkalainen, Philip Scheltens, Frederik Barkhof, Hanneke Rhodius-Meester, Betty Tijms, Afina W. Lemstra, Wiesje van der Flier, Jyrki Lötjönen, Daniel Rueckert
Differential dementia diagnosis on incomplete data with Latent Trees

Amir Alansary, Konstantinos Kamnitsas, Martin Rajchl, Alice Davidson, Christina Malamateniou, Mary Rutherford, Joseph Hajnal, Ben Glocker, Daniel Rueckert, Bernhard Kainz
Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI

Ben Glocker, Ender Konukoglu, Ioannis Lavdas, Juan Eugenio Iglesias, Eric O Aboagye, Andrea G Rockall, Daniel Rueckert
Correction of Fat-Water Swaps in Dixon MRI

Masahiro Oda, Natsuki Shimizu, Kenichi Karasawa, Yukitaka Nimura, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert, Kensaku Mori
Regression Forest-based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation

Ozan Oktay, Wenjia Bai, Matthew Lee, Ricardo Guerrero, Konstantinos Kamnitsas, Jose Caballero, Antonio M Simoes Monteiro de Marvao, Stuart Cook, Declan O’Regan, Daniel Rueckert
Multi-Input Cardiac Image Super-Resolution using Convolutional Neural Networks

Sarah Parisot, Ben Glocker, Markus D Schirmer, Daniel Rueckert
GraMPa: Graph-based Multi-modal Parcellation of the Cortex using Fusion Moves

Christian Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Sandra Smith, Bernhard Kainz, Daniel Rueckert
Real-time Standard Scan Plane Detection and Localisation in Fetal Ultrasound using Fully Convolutional Neural Networks

H. Sokooti Oskooyi, G. Saygili, B. Glocker, B.P.F. Lelieveldt, M. Staring
Accuracy Estimation for Medical Image Registration Using Regression Forests

L. Maier-Hein, T. Ross, J. Groehl, B. Glocker, S. Bodenstedt, C. Stock, E. Heim, H. Kenngott, S. Speidel, K. Maier-Hein
Crowd-algorithm collaboration for large-scale endoscopic image annotation with confidence