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

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

4 December 2018

Yaron Meirovitch

Lu Mi

Hayk Saribekyan

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

6 / 6 papers shown

Title
A Topology-Attention ConvLSTM Network and Its Application to EM Images Jiaqi Yang Xiaoling Hu Chao Chen Chialing Tsai 3DPC MedIm 22 3 0 07 Feb 2022
AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions D. Wei Kisuk Lee Hanyu Li R. Lu J. A. Bae ... Ignacio Arganda-Carreras Brian Matejek N. Kasthuri J. Lichtman Hanspeter Pfister 3DV 52 17 0 12 Jul 2021
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks Alberto Bailoni Constantin Pape Steffen Wolf Anna Kreshuk Fred Hamprecht ISeg 34 7 0 10 Sep 2020
Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction Kisuk Lee R. Lu Kyle L. Luther H. S. Seung 3DV 36 22 0 21 Sep 2019
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration T. Parag Fabian Tschopp William Grisaitis Srinivas C. Turaga Xuewen Zhang Brian Matejek L. Kamentsky J. Lichtman Hanspeter Pfister 34 15 0 27 Jul 2017
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung W. Wong W. Woo 233 7,904 0 13 Jun 2015