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Found 9 papers, 5 papers with code
Two Stream Active Query Suggestion for Active Learning in Connectomics
As a use case, we build an end-to-end active learning framework with our query suggestion method for 3D synapse detection and mitochondria segmentation in connectomics.
Frenet-Serret Frame-based Decomposition for Part Segmentation of 3D Curvilinear Structures
Accurately segmenting 3D curvilinear structures in medical imaging remains challenging due to their complex geometry and the scarcity of diverse, large-scale datasets for algorithm development and evaluation.
TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images
While imaging techniques at macro and mesoscales have garnered substantial attention and resources, microscale Volume Electron Microscopy (vEM) imaging, capable of revealing intricate vascular details, has lacked the necessary benchmarking infrastructure.
PyTorch Connectomics: A Scalable and Flexible Segmentation Framework for EM Connectomics
We present PyTorch Connectomics (PyTC), an open-source deep-learning framework for the semantic and instance segmentation of volumetric microscopy images, built upon PyTorch.
NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale
Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages.
AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions
Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries.
MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images
On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances.
Ranked #2 on
3D Instance Segmentation
on MitoEM
(AP75-R-Test metric)
Icon: An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structures
We present an interactive approach to train a deep neural network pixel classifier for the segmentation of neuronal structures.
Large-Scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images
Segmentation of large-scale electron microscopy data is the main bottleneck in the analysis of these data sets.
