medical-6

Electron Microscopy Image Segmentation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in electron-microscopy-image-segmentation-12

Trend

Dataset

Best Model

Actions

SNEMI3D

EMOrganelles

3D Platelet EM

Libraries

i

Use these libraries to find electron-microscopy-image-segmentation-12 models and implementations

Datasets

3D Platelet EM

Subtasks

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Most implemented papers

Superhuman Accuracy on the SNEMI3D Connectomics Challenge

Peter H. Li, Viren Jain, Kisuk Lee, J. Zung, H. Seung•Tue May 30 2017

This work reports the first submission to surpass the estimate of human accuracy provided by the SNEMI3D leaderboard, and inspires optimism that the goal of full automation may be realizable in the future.

306

Content

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MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy

Joonkee Kim, Gihun Lee, Se-Young Yun, Sangmook Kim•Tue Dec 06 2022

MEDIAR harmonizes data-centric and model-centric approaches as the learning and inference strategies, achieving a 0.9067 F1-score at the validation phase while satisfying the time budget.

26 0

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Large-Scale Electron Microscopy Image Segmentation in Spark

Stephen M. Plaza, S. Berg•Thu Mar 31 2016

A novel strategy to apply image segmentation algorithms to apply on very large datasets that exceed the capacity of a single machine is proposed and is robust to potential segmentation errors which could otherwise severely compromise the quality of the overall segmentation.

15 0

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SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation

T. Tasdizen, Ting Liu, Miaomiao Zhang, Mehran Javanmardi, Nisha Ramesh•Sat Aug 13 2016

A Bayesian model is proposed that combines the supervised and the unsupervised information for probabilistic learning and consistently performs close to the state-of-the-art supervised method with the full labeled data set and significantly outperforms the supervised method with the same labeled subset.

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UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images

H. Urakubo, T. Bullmann, Y. Kubota, Shigeyuki Oba, Shin Ishii•Thu Apr 11 2019

UNI-EM is a software collection for CNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization, which incorporates a set of 2D CNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet.

42 0

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TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain

Yimin Wang, Qi Li, Lijuan Liu, Zhi Zhou, Yun Wang, Lingsheng Kong, Ning Zhong, R. Chai, Xiangfeng Luo, Yike Guo, M. Hawrylycz, Qingming Luo, Zhongze Gu, Wei Xie, Hongkui Zeng, Hanchuan Peng•Sat Apr 27 2019

TeraVR is an open-source virtual reality annotation system for precise and efficient data production of neuronal shapes reconstructed from whole brains that has produced precise 3-D full morphology of long-projecting neurons in whole mouse brains and developed a collaborative workflow for highly accurate neuronal reconstruction.

89 0

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Dense cellular segmentation for EM using 2D–3D neural network ensembles

M. Guay, Z. Emam, Adam Anderson, M. Aronova, B. Storrie, I. Pokrovskaya, R. Leapman•Sun Jan 05 2020

This work presents an algorithm using novel hybrid 2D–3D segmentation networks to produce dense cellular segmentations with accuracy levels that outperform baseline methods and approach those of human annotators, the first published approach to automating the creation of cell models with this level of structural detail.

29 0

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