# Data

@article{thompson2016vivo,
  title={In vivo dendrite regeneration after injury is different from dendrite development},
  author={Thompson-Peer, Katherine L and DeVault, Laura and Li, Tun and Jan, Lily Yeh and Jan, Yuh Nung},
  journal={Genes \& development},
  volume={30},
  number={15},
  pages={1776--1789},
  year={2016},
  publisher={Cold Spring Harbor Lab}
}
@article{nguyen2021comparing,
  title={Comparing automated morphology quantification software on dendrites of uninjured and injured drosophila neurons},
  author={Nguyen, Carolee and Thompson-Peer, Katherine L},
  journal={Neuroinformatics},
  volume={19},
  number={4},
  pages={703--717},
  year={2021},
  publisher={Springer}
}

@inproceedings{shit2021cldice,
  title={clDice-a novel topology-preserving loss function for tubular structure segmentation},
  author={Shit, Suprosanna and Paetzold, Johannes C and Sekuboyina, Anjany and Ezhov, Ivan and Unger, Alexander and Zhylka, Andrey and Pluim, Josien PW and Bauer, Ulrich and Menze, Bjoern H},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16560--16569},
  year={2021}
}

@inproceedings{jin2018ct,
  title={CT-realistic lung nodule simulation from 3D conditional generative adversarial networks for robust lung segmentation},
  author={Jin, Dakai and Xu, Ziyue and Tang, Youbao and Harrison, Adam P and Mollura, Daniel J},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11},
  pages={732--740},
  year={2018},
  organization={Springer}
}

@inproceedings{tom2018simulating,
  title={Simulating patho-realistic ultrasound images using deep generative networks with adversarial learning},
  author={Tom, Francis and Sheet, Debdoot},
  booktitle={2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018)},
  pages={1174--1177},
  year={2018},
  organization={IEEE}
}

@inproceedings{lau2018scargan,
  title={Scargan: chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans},
  author={Lau, Felix and Hendriks, Tom and Lieman-Sifry, Jesse and Sall, Sean and Golden, Dan},
  booktitle={Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4},
  pages={343--350},
  year={2018},
  organization={Springer}
}

@inproceedings{mok2019learning,
  title={Learning data augmentation for brain tumor segmentation with coarse-to-fine generative adversarial networks},
  author={Mok, Tony CW and Chung, Albert CS},
  booktitle={Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4},
  pages={70--80},
  year={2019},
  organization={Springer}
}






@article{ascoli2007neuromorpho,
  title={NeuroMorpho. Org: a central resource for neuronal morphologies},
  author={Ascoli, Giorgio A and Donohue, Duncan E and Halavi, Maryam},
  journal={Journal of Neuroscience},
  volume={27},
  number={35},
  pages={9247--9251},
  year={2007},
  publisher={Soc Neuroscience}
}

# Other

@article{longair2011simple,
  title={Simple Neurite Tracer: open source software for reconstruction, visualization and analysis of neuronal processes},
  author={Longair, Mark H and Baker, Dean A and Armstrong, J Douglas},
  journal={Bioinformatics},
  volume={27},
  number={17},
  pages={2453--2454},
  year={2011},
  publisher={Oxford University Press}
}


# Noise based generation

@article{chen2021deep,
  title={Deep-learning-based automated neuron reconstruction from 3D microscopy images using synthetic training images},
  author={Chen, Weixun and Liu, Min and Du, Hao and Radojevi{\'c}, Miroslav and Wang, Yaonan and Meijering, Erik},
  journal={IEEE Transactions on Medical Imaging},
  volume={41},
  number={5},
  pages={1031--1042},
  year={2021},
  publisher={IEEE}
}

@article{radojevic2019automated,
  title={Automated neuron reconstruction from 3D fluorescence microscopy images using sequential Monte Carlo estimation},
  author={Radojevi{\'c}, Miroslav and Meijering, Erik},
  journal={Neuroinformatics},
  volume={17},
  number={3},
  pages={423--442},
  year={2019},
  publisher={Springer}
}

@article{hamarneh2010vascusynth,
  title={VascuSynth: Simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis},
  author={Hamarneh, Ghassan and Jassi, Preet},
  journal={Computerized medical imaging and graphics},
  volume={34},
  number={8},
  pages={605--616},
  year={2010},
  publisher={Elsevier}
}

@article{tetteh2020deepvesselnet,
  title={Deepvesselnet: Vessel segmentation, centerline prediction, and bifurcation detection in 3-d angiographic volumes},
  author={Tetteh, Giles and Efremov, Velizar and Forkert, Nils D and Schneider, Matthias and Kirschke, Jan and Weber, Bruno and Zimmer, Claus and Piraud, Marie and Menze, Bj{\"o}rn H},
  journal={Frontiers in Neuroscience},
  volume={14},
  pages={1285},
  year={2020},
  publisher={Frontiers}
}


@inproceedings{prabhakar2024vesselformer,
  title={Vesselformer: Towards Complete 3D Vessel Graph Generation from Images},
  author={Prabhakar, Chinmay and Shit, Suprosanna and Paetzold, Johannes C and Ezhov, Ivan and Koner, Rajat and Li, Hongwei and Kofler, Florian Sebastian and Menze, Bjoern},
  booktitle={Medical Imaging with Deep Learning},
  pages={320--331},
  year={2024},
  organization={PMLR}
}

@article{zhou2018deepneuron,
  title={DeepNeuron: an open deep learning toolbox for neuron tracing},
  author={Zhou, Zhi and Kuo, Hsien-Chi and Peng, Hanchuan and Long, Fuhui},
  journal={Brain informatics},
  volume={5},
  pages={1--9},
  year={2018},
  publisher={Springer}
}


# Deep learning based generation methods

@inproceedings{mao2019mode,
  title={Mode seeking generative adversarial networks for diverse image synthesis},
  author={Mao, Qi and Lee, Hsin-Ying and Tseng, Hung-Yu and Ma, Siwei and Yang, Ming-Hsuan},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={1429--1437},
  year={2019}
}

@inproceedings{isola2017image,
  title={Image-to-image translation with conditional adversarial networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1125--1134},
  year={2017}
}

@inproceedings{wang2018high,
  title={High-resolution image synthesis and semantic manipulation with conditional gans},
  author={Wang, Ting-Chun and Liu, Ming-Yu and Zhu, Jun-Yan and Tao, Andrew and Kautz, Jan and Catanzaro, Bryan},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={8798--8807},
  year={2018}
}


@inproceedings{uzunova2019multi,
  title={Multi-scale gans for memory-efficient generation of high resolution medical images},
  author={Uzunova, Hristina and Ehrhardt, Jan and Jacob, Fabian and Frydrychowicz, Alex and Handels, Heinz},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13--17, 2019, Proceedings, Part VI 22},
  pages={112--120},
  year={2019},
  organization={Springer}
}

@inproceedings{laxman2021efficient,
  title={Efficient high-resolution image-to-image translation using multi-scale gradient U-net},
  author={Laxman, Kumarapu and Dubey, Shiv Ram and Kalyan, Baddam and Kojjarapu, Satya Raj Vineel},
  booktitle={International Conference on Computer Vision and Image Processing},
  pages={33--44},
  year={2021},
  organization={Springer}
}

@article{costa2017end,
  title={End-to-end adversarial retinal image synthesis},
  author={Costa, Pedro and Galdran, Adrian and Meyer, Maria Ines and Niemeijer, Meindert and Abr{\`a}moff, Michael and Mendon{\c{c}}a, Ana Maria and Campilho, Aur{\'e}lio},
  journal={IEEE transactions on medical imaging},
  volume={37},
  number={3},
  pages={781--791},
  year={2017},
  publisher={IEEE}
}

@article{denton2015deep,
  title={Deep generative image models using a laplacian pyramid of adversarial networks},
  author={Denton, Emily L and Chintala, Soumith and Fergus, Rob and others},
  journal={Advances in neural information processing systems},
  volume={28},
  year={2015}
}


@ARTICLE{Peng2015BigNeuron,
  author = {Hanchuan Peng and Michael Hawrylycz and Jane Roskams and Sean Hill and Nelson Spruston and Erik Meijering and Giorgio A. Ascoli},
  title = {{BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images}},
  journal = {Neuron},
  year = {2015},
  volume = {87},
  pages = {252-256},
  number = {2},
  abstract = {Understanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons.},
  doi = {https://doi.org/10.1016/j.neuron.2015.06.036},
  issn = {0896-6273},
  timestamp = {2022.11.06 11:52:505}
}

@ARTICLE{Nardelli2020Generative,
  author = {Pietro Nardelli and James C. Ross and Raúl {San José Estépar}},
  title = {Generative-based airway and vessel morphology quantification on chest CT images},
  journal = {Medical Image Analysis},
  year = {2020},
  volume = {63},
  pages = {101691},
  issn = {1361-8415},
  keywords = {Lung, Airway, Vessel, Deep-learning regression, Subvoxel resolution},
  timestamp = {2024.02.06 19:18:485},
}

@INPROCEEDINGS{Ronneberger2015U,
  author = {Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas},
  title = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015},
  year = {2015},
  editor = {Navab, Nassir
and Hornegger, Joachim
and Wells, William M.
and Frangi, Alejandro F.},
  pages = {234--241},
  address = {Cham},
  publisher = {Springer International Publishing},
  abstract = {There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.},
  isbn = {978-3-319-24574-4},
}

