IJCARS Best Paper Award


The MICCAI IJCARS Best Paper Award recognizes the highest quality paper in the Special Issue on the main MICCAI conference published in the International Journal of Computer Assisted Radiology and Surgery (IJCARS) journal. Typically one award is issued each year, but the journal may decide to split the award between two winners.

The definition of and guidelines for this award can be found here.

Winners of the IJCARS Best Paper Award

2019

Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Russell H. Taylor
Endoscopic navigation in the clinic: registration in the absence of preoperative imaging

IJCARS2019A

Bastian Bier, Florian Goldmann, Jan-Nico Zaech, Javad Fotouhi, Rachel Hegeman, Robert Grupp, Mehran Armand, Greg Osgood, Nassir Navab, Andreas Maier, Mathias Unberath
Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views

IJCARS2019B

Runners up:

W. Mandel, O. Turcot, D. Knez, S. Parent, S.Kadoury
Prediction outcomes for anterior vertebral body growth modulation surgery from discriminant spatiotemporal manifolds

J. Fotouhi, M. Unberath, T. Song, J. Hajek, S. C. Lee, B. Bier, A. Maier, G. Osgood, M. Armand, N. Navab
Co-localized augmented human and X-ray observers in collaborative surgical ecosystem

2018

Arash Pourtaherian, Farhad Ghazvinian Zanjani, Svitlana Zinger, Nenad Mihajlovic, Gary C Ng, Hendrikus HM Korsten, Peter HN de With
Robust and semantic needle detection in 3D ultrasound using orthogonal-plane convolutional neural networks

2017

Shekoofeh Azizi, Sharareh Bayat, Pingkun Yan, Amir Tahmasebi, Guy Nir, Jin Tae Kwak, Sheng Xu, Storey Wilson, Kenneth A. Iczkowski, M. Scott Lucia, Larry Goldenberg, Septimiu E. Salcudean, Peter A. Pinto, Bradford Wood, Purang Abolmaesumi, Parvin Mousavi
Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations

2016

Marco Esposito, Benjamin Busam, Christoph Hennersperger, Julia Rackerseder, Nassir Navab, Benjamin Frisch
Multimodal US-Gamma Imaging using Collaborative Robotics for Cancer Staging Biopsies