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20172026
most citedMorphological Error Detection in 3D Segmentations

20 citations · 46 across the 21 of their papers we have counts for

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7 papers · 1 filter

cs.CV2025

Cascade Detector Analysis and Application to Biomedical Microscopy

Thomas L. Athey, Shashata Sawmya, Nir Shavit

As both computer vision models and biomedical datasets grow in size, there is an increasing need for efficient inference algorithms. We utilize cascade detectors to efficiently ide…

cs.CV2025

NeuroADDA: Active Discriminative Domain Adaptation in Connectomic

Shashata Sawmya, Thomas L. Athey, Gwyneth Liu +1

Training segmentation models from scratch has been the standard approach for new electron microscopy connectomics datasets. However, leveraging pretrained models from existing data…

cs.CV20231 cited

The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons

Tri Nguyen, Mukul Narwani, Mark Larson +9

The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabl…

cs.CV2021

HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps

Lu Mi, Hang Zhao, Charlie Nash +7

High Definition (HD) maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. They are critical for several key stages in an autonomous drivin…

cs.CV2018

Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics

Yaron Meirovitch, Lu Mi, Hayk Saribekyan +3

Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object tracking or instance segmentation followed by o…

cs.CV201720 cited

Morphological Error Detection in 3D Segmentations

David Rolnick, Yaron Meirovitch, Toufiq Parag +5

Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology. This leads to a high incidence of erroneously merged objects. Humans,…