@ARTICLE{xiao2018mito,
  AUTHOR={Xiao, Chi and Chen, Xi and Li, Weifu and Li, Linlin and Wang, Lu and Xie, Qiwei and Han, Hua},   
  TITLE={Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network},      
  JOURNAL={Frontiers in Neuroanatomy},      
  VOLUME={12},      
  PAGES={92},     
  YEAR={2018},
  URL={https://www.frontiersin.org/article/10.3389/fnana.2018.00092},
  DOI={10.3389/fnana.2018.00092},      
  ISSN={1662-5129}
}

@inproceedings{jaderberg2015spatial,
  title={Spatial Transformer Networks},
  author={Jaderberg, Max and Simonyan, Karen and Zisserman, Andrew and others},
  booktitle={NeurIPS},
  pages={2017--2025},
  year={2015}
}

@article{lucchi2012supervoxel,
  title={Supervoxel-Based Segmentation of Mitochondria in
EM Image Stacks with Learned Shape Features},
  author={Lucchi, Aur{\'e}lien and Smith, Kevin and Achanta, Radhakrishna and Knott, Graham and Fua, Pascal},
  journal={IEEE Transactions on Medical Imaging},
  volume={31},
  number={2},
  pages={474--486},
  year={2012},
  publisher={IEEE}
}

@incollection{lucchi2012structured,
  title={Structured Image Segmentation using Kernelized Features},
  author={Lucchi, Aur{\'e}lien and Li, Yunpeng and Smith, Kevin and Fua, Pascal},
  booktitle={ECCV},
  pages={400--413},
  year={2012},
  publisher={Springer}
}

@inproceedings{lucchi2013learning,
  title={Learning for Structured Prediction using Approximate Subgradient Descent with Working Sets},
  author={Lucchi, Aur{\'e}lien and Li, Yunpeng and Fua, Pascal},
  booktitle={CVPR},
  pages={1987--1994},
  year={2013},
  organization={IEEE}
}

@inproceedings{lucchi2014exploiting,
  title={Exploiting Enclosing Membranes and Contextual Cues for Mitochondria Segmentation},
  author={Lucchi, Aur{\'e}lien and Becker, Carlos and Neila, Pablo M{\'a}rquez and Fua, Pascal},
  booktitle={MICCAI},
  pages={65--72},
  year={2014},
  organization={Springer}
}

@inproceedings{oztel2017mitochondria,
  title={Mitochondria Segmentation in Electron Microscopy Volumes using Deep Convolutional Neural Network},
  author={Oztel, Ismail and Yolcu, Gozde and Ersoy, Ilker and White, Tommi and Bunyak, Filiz},
  booktitle={International Conference on Bioinformatics and Biomedicine},
  pages={1195--1200},
  year={2017}
}

@inproceedings{ciresan2012deep,
  title={Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images},
  author={Ciresan, Dan and Giusti, Alessandro and Gambardella, Luca M and Schmidhuber, J{\"u}rgen},
  booktitle={NeurIPS},
  pages={2843--2851},
  year={2012}
}

@inproceedings{marquez2014non,
  title={Non-parametric Higher-Order Random Fields for Image Segmentation},
  author={M{\'a}rquez-Neila, Pablo and Kohli, Pushmeet and Rother, Carsten and Baumela, Luis},
  booktitle={ECCV},
  pages={269--284},
  year={2014},
  organization={Springer}
}

@inproceedings{cheng2017volume,
  title={Volume Segmentation Using Convolutional Neural Networks with Limited Training Data},
  author={Cheng, Hsueh-Chien and Varshney, Amitabh},
  booktitle={Image Processing (ICIP), IEEE International Conference on},
  pages={590--594},
  year={2017},
}

@article{quan2016fusionnet,
  title={FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics},
  author={Quan, Tran Minh and Hilderbrand, David GC and Jeong, Won-Ki},
  journal={arXiv preprint arXiv:1612.05360},
  year={2016}
}


@inproceedings{cciccek20163d,
  title={3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation},
  author={{\c{C}}i{\c{c}}ek, {\"O}zg{\"u}n and Abdulkadir, Ahmed and Lienkamp, Soeren S and Brox, Thomas and Ronneberger, Olaf},
  booktitle={MICCAI},
  pages={424--432},
  year={2016},
  organization={Springer}
}

@article{lucchi2015learning,
  title={Learning Structured Models for Segmentation of 2-D and 3-D Imagery},
  author={Lucchi, Aur{\'e}lien and M{\'a}rquez-Neila, Pablo and Becker, Carlos and Li, Yunpeng and Smith, Kevin and Knott, Graham and Fua, Pascal},
  journal={IEEE Transactions on Medical Imaging},
  volume={34},
  number={5},
  pages={1096--1110},
  year={2015},
  publisher={IEEE}
}

@inproceedings{vitaladevuni2008mitochondria,
  title={Mitochondria Detection in Electron Microscopy Images},
  author={Vitaladevuni, Shiv and Mishchenko, Yuriy and Genkin, Alex and Chklovskii, Dmitri and Harris, Kristen},
  booktitle={Workshop on Microscopic Image Analysis with Applications in Biology},
  volume={42},
  year={2008}
}

@article{narasimha2009automatic,
  title={Automatic Joint Classification and Segmentation of Whole Cell 3D Images},
  author={Narasimha, Rajesh and Ouyang, Hua and Gray, Alexander and McLaughlin, Steven W and Subramaniam, Sriram},
  journal={Pattern Recognition},
  volume={42},
  number={6},
  pages={1067--1079},
  year={2009},
  publisher={Elsevier}
}

@article{perez2014workflow,
  title={A Workflow for the Automatic Segmentation of Organelles in Electron Microscopy Image Stacks},
  author={Perez, Alex J. and Seyedhosseini, Mojtaba and Deerinck, Thomas J and Bushong, Eric A and Panda, Satchidananda and Tasdizen, Tolga and Ellisman, Mark H},
  journal={Frontiers in neuroanatomy},
  volume={8},
  pages={126},
  year={2014},
  publisher={Frontiers}
}

@article{jaume2012multiscale,
  title={A Multiscale Parallel Computing Architecture for Automated Segmentation of the Brain Connectome},
  author={Jaume, Sylvain and Knobe, Kathleen and Newton, Ryan R. and Schlimbach, Frank and Blower, Melanie and Reid, R Clay},
  journal={IEEE Transactions on Biomedical Engineering},
  volume={59},
  number={1},
  pages={35--38},
  year={2012},
  publisher={IEEE}
}

@inproceedings{smith2009fast,
  title={Fast Ray Features for Learning Irregular Shapes},
  author={Smith, Kevin and Carleton, Alan and Lepetit, Vincent},
  booktitle={CVPR},
  pages={397--404},
  year={2009},
  organization={IEEE}
}

@inproceedings{kumar2010radon,
  title={Radon-Like Features and their Application to Connectomics},
  author={Kumar, Ritwik and V{\'a}zquez-Reina, Amelio and Pfister, Hanspeter},
  booktitle={CVPR Workshop},
  pages={186--193},
  year={2010},
  organization={IEEE}
}

@inproceedings{vazquez2011segmentation,
  title={Segmentation Fusion for Connectomics},
  author={Vazquez-Reina, Amelio and Gelbart, Michael and Huang, Daniel and Lichtman, Jeff and Miller, Eric and Pfister, Hanspeter},
  booktitle={ICCV},
  pages={177--184},
  year={2011},
  organization={IEEE}
}

@inproceedings{seyedhosseini2013segmentation,
  title={Segmentation of Mitochondria in Electron Microscopy Images using Algebraic Curves},
  author={Seyedhosseini, Mojtaba and Ellisman, Mark H and Tasdizen, Tolga},
  booktitle={Biomedical Imaging (ISBI)},
  pages={860--863},
  year={2013},
  organization={IEEE}
}


@inproceedings{long2015fully,
  title={Fully Convolutional Networks for Semantic Segmentation},
  author={Long, Jonathan and Shelhamer, Evan and Darrell, Trevor},
  booktitle={CVPR},
  pages={3431--3440},
  year={2015}
}

@inproceedings{ronneberger2015u,
  title={U-Net: Convolutional Networks for Biomedical Image Segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={MICCAI},
  pages={234--241},
  year={2015},
  organization={Springer}
}

@article{kasthuri2015saturated,
  title={Saturated Reconstruction of a Volume of Neocortex},
  author={Kasthuri, Narayanan and Hayworth, Kenneth Jeffrey and Berger, Daniel Raimund and Schalek, Richard Lee and Conchello, Jos{\'e} Angel and Knowles-Barley, Seymour and Lee, Dongil and V{\'a}zquez-Reina, Amelio and Kaynig, Verena and Jones, Thouis Raymond and others},
  journal={Cell},
  volume={162},
  number={3},
  pages={648--661},
  year={2015},
  publisher={Elsevier}
}

@article{kaynig2015large,
  title={Large-scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images},
  author={Kaynig, Verena and Vazquez-Reina, Amelio and Knowles-Barley, Seymour and Roberts, Mike and Jones, Thouis R and Kasthuri, Narayanan and Miller, Eric and Lichtman, Jeff and Pfister, Hanspeter},
  journal={Medical Image Analysis},
  volume={22},
  number={1},
  pages={77--88},
  year={2015},
  publisher={Elsevier}
}

@ARTICLE{haehn_dojo_2014,
author = {D. Haehn and S. Knowles-Barley and M. Roberts and J. Beyer and N. Kasthuri and J.W. Lichtman and H. Pfister},
title = {Design and Evaluation of Interactive Proofreading Tools for Connectomics},
journal = {IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE SciVis 2014)},
year = {2014},
volume = {20},
number = {12},
pages = {2466--2475},
} 

@ARTICLE{jeff_science,
  author = {Jeff W. Lichtman and W. Denk},
  title = {The Big and the Small: Challenges of Imaging the Brain's Circuits},
  journal = {Science},
  year = {2011},
  volume = {334},
  pages = {618--623},
  number = {6056}
}

@ARTICLE{Helmstaedter2011,
  author = {Helmst{\"a}dter, Moritz and Briggman, Kevin L and Denk, Winfried},
  title = {High-accuracy Neurite Reconstruction for High-throughput Neuroanatomy.},
  journal = {Nature Neuroscience},
  year = {2011},
  volume = {14},
  pages = {1081--1088},
  number = {8},
  publisher = {Nature Publishing Group}
}

@inproceedings{haehn2017scalable,
	title={Scalable Interactive Visualization for Connectomics},
	author={Haehn, Daniel and Hoffer, John and Matejek, Brian and Suissa-Peleg, Adi and Al-Awami, Ali K and Kamentsky, Lee and Gonda, Felix and Meng, Eagon and Zhang, William and Schalek, Richard and others},
	booktitle={Informatics},
	volume={4},
	pages={29},
	year={2017},
	organization={Multidisciplinary Digital Publishing Institute}
}


@article{schalek2016,
  title={Imaging a 1 mm 3 Volume of Rat Cortex Using a MultiBeam SEM},
  author={Schalek, Richard and Lee, Dong and Kasthuri, Narayanan and Peleg, Adi and Jones, Thouis R. and Kaynig, Verena and Haehn, Daniel and Pfister, Hanspeter and Cox, David and Lichtman, Jeff W.},
  journal={Microscopy and Microanalysis},
  volume={22},
  number={S3},
 pages={582--583},
  year={2016},
  publisher={Cambridge University Press}
}
@article{suissa2016,
  title={Automatic Neural Reconstruction from Petavoxel of Electron Microscopy Data},
  author={Suissa-Peleg, Adi and Haehn, Daniel and Knowles-Barley, Seymour and Kaynig, Verena and Jones, Thouis R and Wilson, Alyssa and Schalek, Richard and Lichtman, Jeffery W and Pfister, Hanspeter},
  journal={Microscopy and Microanalysis},
  volume={22},
  number={S3},
  pages={536--537},
  year={2016},
  publisher={Cambridge University Press}
}

@Article{Lichtman2014,
author={Lichtman, Jeff W.
and Pfister, Hanspeter
and Shavit, Nir},
title={The Big Data Challenges of Connectomics},
journal={Nat Neurosci},
year={2014},
month={Nov},
publisher={Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
volume={17},
number={11},
pages={1448-1454},
abstract={The structure of the nervous system is extraordinarily complicated because individual neurons are interconnected to hundreds or even thousands of other cells in networks that can extend over large volumes. Mapping such networks at the level of synaptic connections, a field called connectomics, began in the 1970s with a the study of the small nervous system of a worm and has recently garnered general interest thanks to technical and computational advances that automate the collection of electron-microscopy data and offer the possibility of mapping even large mammalian brains. However, modern connectomics produces 'big data', unprecedented quantities of digital information at unprecedented rates, and will require, as with genomics at the time, breakthrough algorithmic and computational solutions. Here we describe some of the key difficulties that may arise and provide suggestions for managing them.},
note={Perspective},
issn={1097-6256}
}

@ARTICLE{GALA2014,
 AUTHOR={Nunez-Iglesias, Juan  and  Kennedy, Ryan  and  Plaza, Stephen M  and  Chakraborty, Anirban  and  Katz, William T},   
 TITLE={Graph-based Active Learning of Agglomeration {(GALA)}: A Python library to segment {2D} and {3D} neuroimages},      
 JOURNAL={Frontiers in Neuroinformatics},      
 VOLUME={8},      
 YEAR={2014},      
 NUMBER={34},     
 ISSN={1662-5196} ,      
 ABSTRACT={The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.}
}

@article{cetina2018multi,
  title={Multi-class Segmentation of Neuronal Structures in Electron Microscopy Images},
  author={Cetina, Kendrick and Buenaposada, Jos{\'e} M and Baumela, Luis},
  journal={BMC Bioinformatics},
  volume={19},
  number={1},
  pages={298},
  year={2018},
  publisher={BioMed Central}
}

@article{zeviani2004mitochondrial,
  title={Mitochondrial Disorders},
  author={Zeviani, Massimo and Di Donato, Stefano},
  journal={Brain},
  volume={127},
  number={10},
  pages={2153--2172},
  year={2004},
  publisher={Oxford University Press}
}

@book{darnell1990molecular,
  title={Molecular Cell Biology},
  author={Darnell, James E and Lodish, Harvey and Baltimore, David and others},
  volume={2},
  year={1990},
  publisher={Scientific American Books New York}
}

@article{lee2017superhuman,
  title={Superhuman Accuracy on the SNEMI3D Connectomics Challenge},
  author={Lee, Kisuk and Zung, Jonathan and Li, Peter and Jain, Viren and Seung, H Sebastian},
  journal={arXiv preprint arXiv:1706.00120},
  year={2017}
}


@article{mekuvc2020automatic,
  title={Automatic segmentation of mitochondria and endolysosomes in volumetric electron microscopy data},
  author={Meku{\v{c}}, Manca {\v{Z}}erovnik and Bohak, Ciril and Hudoklin, Samo and Kim, Byeong Hak and Kim, Min Young and Marolt, Matija and others},
  journal={Computers in Biology and Medicine},
  pages={103693},
  year={2020},
  publisher={Elsevier}
}

@article{urakubo2019uni,
  title={Uni-eM: An environment for Deep neural network-Based Automated Segmentation of neuronal electron Microscopic images},
  author={Urakubo, Hidetoshi and Bullmann, Torsten and Kubota, Yoshiyuki and Oba, Shigeyuki and Ishii, Shin},
  journal={Scientific Reports},
  volume={9},
  number={1},
  pages={1--9},
  year={2019},
  publisher={Nature Publishing Group}
}
