10 citations · 22 across the 6 of their papers we have counts for
6 papers
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
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…
Panoptic segmentation with highly imbalanced semantic labels
Josef Lorenz Rumberger, Elias Baumann, Peter Hirsch +3
We describe here the panoptic segmentation method we devised for our participation in the CoNIC: Colon Nuclei Identification and Counting Challenge at ISBI 2022. Key features of ou…
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…
An Auxiliary Task for Learning Nuclei Segmentation in 3D Microscopy Images
Peter Hirsch, Dagmar Kainmueller
Segmentation of cell nuclei in microscopy images is a prevalent necessity in cell biology. Especially for three-dimensional datasets, manual segmentation is prohibitively time-cons…
PatchPerPix for Instance Segmentation
Peter Hirsch, Lisa Mais, Dagmar Kainmueller
We present a novel method for proposal free instance segmentation that can handle sophisticated object shapes which span large parts of an image and form dense object clusters with…