239 citations · 253 across the 9 of their papers we have counts for
12 papers
Catena: A Comprehensive Software Suite for Large-Scale Connectomics
Samia Mohinta, Pedro Gómez-Gálvez, Shi Yan Lee +5
The gold standard datasets for mapping connectomes are electron microscopy volumes of densely labeled neural tissue at nanometer resolution. Yet reconstructing and proofreading neu…
Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages
Peter Hirsch, Caroline Malin-Mayor, Anthony Santella +3
Tracking all nuclei of an embryo in noisy and dense fluorescence microscopy data is a challenging task. We build upon a recent method for nuclei tracking that combines weakly-super…
Discriminative Attribution from Counterfactuals
Nils Eckstein, Alexander S. Bates, Gregory S. X. E. Jefferis +1
We present a method for neural network interpretability by combining feature attribution with counterfactual explanations to generate attribution maps that highlight the most discr…
How Shift Equivariance Impacts Metric Learning for Instance Segmentation
Josef Lorenz Rumberger, Xiaoyan Yu, Peter Hirsch +6
Metric learning has received conflicting assessments concerning its suitability for solving instance segmentation tasks. It has been dismissed as theoretically flawed due to the sh…
Microtubule Tracking in Electron Microscopy Volumes
Nils Eckstein, Julia Buhmann, Matthew Cook +1
We present a method for microtubule tracking in electron microscopy volumes. Our method first identifies a sparse set of voxels that likely belong to microtubules. Similar to prior…
Instance Separation Emerges from Inpainting
Steffen Wolf, Fred A. Hamprecht, Jan Funke
Deep neural networks trained to inpaint partially occluded images show a deep understanding of image composition and have even been shown to remove objects from images convincingly…