no code implementations • • Zudi Lin, Donglai Wei, Won-Dong Jang, Siyan Zhou, Xupeng Chen, Xueying Wang, Richard Schalek, Daniel Berger, Brian Matejek, Lee Kamentsky, Adi Peleg, Daniel Haehn, Thouis Jones, Toufiq Parag, Jeff Lichtman, Hanspeter Pfister
As a use case, we build an end-to-end active learning framework with our query suggestion method for 3D synapse detection and mitochondria segmentation in connectomics.
Tractography from high-dimensional diffusion magnetic resonance imaging (dMRI) data allows brain's structural connectivity analysis.
Fiber tracking produces large tractography datasets that are tens of gigabytes in size consisting of millions of streamlines.
High-resolution connectomics data allows for the identification of dysfunctional mitochondria which are linked to a variety of diseases such as autism or bipolar.
Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading.
We present an interactive approach to train a deep neural network pixel classifier for the segmentation of neuronal structures.