52 citations · 79 across the 12 of their papers we have counts for
12 papers
Tiling artifacts and trade-offs of feature normalization in the segmentation of large biological images
Elena Buglakova, Anwai Archit, Edoardo D'Imprima +3
Segmentation of very large images is a common problem in microscopy, medical imaging or remote sensing. The problem is usually addressed by sliding window inference, which can theo…
Probabilistic Domain Adaptation for Biomedical Image Segmentation
Anwai Archit, Constantin Pape
Segmentation is a crucial analysis task in biomedical imaging. Given the diverse experimental settings in this field, the lack of generalization limits the use of deep learning in…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Stateless actor-critic for instance segmentation with high-level priors
Paul Hilt, Maedeh Zarvandi, Edgar Kaziakhmedov +4
Instance segmentation is an important computer vision problem which remains challenging despite impressive recent advances due to deep learning-based methods. Given sufficient trai…
Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings
Adrian Wolny, Qin Yu, Constantin Pape +1
Most state-of-the-art instance segmentation methods have to be trained on densely annotated images. While difficult in general, this requirement is especially daunting for biomedic…
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks
Alberto Bailoni, Constantin Pape, Steffen Wolf +2
This work introduces a new proposal-free instance segmentation method that builds on single-instance segmentation masks predicted across the entire image in a sliding window style.…
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation
Steffen Wolf, Yuyan Li, Constantin Pape +3
Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label. In commonly used pipeline…
Synthetic patches, real images: screening for centrosome aberrations in EM images of human cancer cells
Artem Lukoyanov, Isabella Haberbosch, Constantin Pape +3
Recent advances in high-throughput electron microscopy imaging enable detailed study of centrosome aberrations in cancer cells. While the image acquisition in such pipelines is aut…
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation
Alberto Bailoni, Constantin Pape, Nathan Hütsch +4
We propose a theoretical framework that generalizes simple and fast algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive inter…
Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts
Constantin Pape, Alex Matskevych, Adrian Wolny +7
The throughput of electron microscopes has increased significantly in recent years, enabling detailed analysis of cell morphology and ultrastructure. Analysis of neural circuits at…
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning
Steffen Wolf, Alberto Bailoni, Constantin Pape +4
Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either…
Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain
Larissa Heinrich, Jan Funke, Constantin Pape +2
Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron…