New FXAA Anti-Aliasing Option in ParaView/VTK

Kitware is a Bronze Sponsor of MICCAI, which will draw attendees from the biomedical, engineering, and clinical fields who will take part in presentations; poster sessions; and satellite workshops, tutorials, and challenges. Kitware is also a sponsor of the Young Scientist Publication Impact Award. Information on last year’s award is available on the Kitware blog.

Stephen Aylward, the senior director of medical research and senior director of operations – North Carolina at Kitware, will be among those attending the conference. He is a co-author on a paper that was accepted to Simulation and Synthesis in Medical Imaging (SASHIMI) 2016, which will be held along with MICCAI. He will be available throughout MICCAI to interview interested job and internship candidates on the spot. Please visit the Kitware employment site for a list of available positions, and contact kitware@kitware.com with questions.

SASHIMI 2016 is a half-day workshop that is part of the International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI). The paper “Registration of Pathological Images” has been accepted to the workshop. Its authors include Xiao Yang (University of North Carolina at Chapel Hill), Xu Han, Eunbyung Park (UNC Chapel Hill), Stephen Aylward (Kitware), Roland Kwitt (University of Salzburg), and Marc Niethammer (UNC Chapel Hill/Biomedical Research Imaging Center).

Abstract from “Proceedings of the International Workshop on Simulation and Synthesis in Medical Imaging (SASHIMI) 2016”
Volume Editors: Sotirios A Tsaftaris, Ali Gooya, Alejandro F Frangi, and Jerry L Prince

This paper proposes an approach to improve atlas-to-image registration accuracy with large pathologies. Instead of directly registering an atlas to a pathological image, the method learns a mapping from the pathological image to a quasi-normal image, for which more accurate
registration is possible. Specifically, the method uses a deep variational convolutional encoder-decoder network to learn the mapping. Furthermore, the method estimates local mapping uncertainty through network inference statistics and uses those estimates to down-weight the image
registration similarity measure in areas of high uncertainty. The performance of the method is quantified using synthetic brain tumor images and images from the brain tumor segmentation challenge (BRATS 2015).

Menze, B.H., Reyes, M., Leemput, K.V.: The multimodal brain tumor image segmentation benchmark (BRATS). TMI 34(10), 1993{2024 (2015)