Neuron reconstruction algorithms used in electron microscope volumes have received
increasing attention in recent years. Most current methods are highly reliant on neuron …

Most contemporary supervised image segmentation methods do not preserve the initial
topology of the given input (like the closeness of the contours). One can generally remark …

We propose a theoretical framework that generalizes simple and fast algorithms for
hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive …

Advances in image segmentation techniques, brought by convolutional neural network
(CNN) architectures like U-Net, show promise for tasks such as automated cancer …

Capturing the global topology of an image is essential for proposing an accurate
segmentation of its domain. However, most of existing segmentation methods do not …

We propose a theoretical framework that generalizes simple and fast algorithms for
hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive …

We improve on an existing instance segmentation model with a probabilistic extension to the
encoded neighbourhood branch model (Bailoni et al., 2020)-we call it multiple outputs …

Currently, most state-of-the-art pipelines for 3D micro-connectomic reconstruction deal with
neuron over-segmentation, agglomeration and subcellular compartment (nuclei …

Most contemporary supervised image segmentation methods do not preserve the initial
topology of the given input (like the closeness of the contours). One can generally remark …