%Visceral data
@article{jimenez2016cloud,
  title={Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks},
  author={Jimenez-del-Toro, Oscar and M{\"u}ller, Henning and Krenn, Markus and Gruenberg, Katharina and Taha, Abdel Aziz and Winterstein, Marianne and Eggel, Ivan and Foncubierta-Rodr{\'\i}guez, Antonio and Goksel, Orcun and Jakab, Andr{\'a}s and others},
  journal={IEEE transactions on medical imaging},
  volume={35},
  number={11},
  pages={2459--2475},
  year={2016},
  publisher={IEEE}
}

%registration 1
@article{rohlfing2004evaluation,
  title={Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains},
  author={Rohlfing, Torsten and Brandt, Robert and Menzel, Randolf and Maurer Jr, Calvin R},
  journal={NeuroImage},
  volume={21},
  number={4},
  pages={1428--1442},
  year={2004},
  publisher={Elsevier}
}
%registration 2
@article{van2008hippocampus,
  title={Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts},
  author={van der Lijn, Fedde and Den Heijer, Tom and Breteler, Monique MB and Niessen, Wiro J},
  journal={Neuroimage},
  volume={43},
  number={4},
  pages={708--720},
  year={2008},
  publisher={Elsevier}
}
%registration 3
@article{isgum2009multi,
  title={Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in CT scans},
  author={Isgum, Ivana and Staring, Marius and Rutten, Annemarieke and Prokop, Mathias and Viergever, Max A and Van Ginneken, Bram},
  journal={IEEE transactions on medical imaging},
  volume={28},
  number={7},
  pages={1000--1010},
  year={2009},
  publisher={IEEE}
}

% registration 4
@article{kiricsli2010evaluation,
  title={Evaluation of a multi-atlas based method for segmentation of cardiac CTA data: a large-scale, multicenter, and multivendor study},
  author={Kiri{\c{s}}li, HA and Schaap, Michiel and Klein, Stefan and Papadopoulou, Stella-Lida and Bonardi, Mara and Chen, Chin-Hui and Weustink, Annick C and Mollet, Nico R and Vonken, Evert-Jan and van der Geest, Rob J and others},
  journal={Medical physics},
  volume={37},
  number={12},
  pages={6279--6291},
  year={2010},
  publisher={Wiley Online Library}
}

% registration 5
@inproceedings{ranjan2011organ,
  title={Organ localization through anatomy-aware non-rigid registration with atlas},
  author={Ranjan, Sohan R},
  booktitle={2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)},
  pages={1--5},
  year={2011},
  organization={IEEE}
}
}
% machine learning paper
@article{criminisi2013regression,
  title={Regression forests for efficient anatomy detection and localization in computed tomography scans},
  author={Criminisi, Antonio and Robertson, Duncan and Konukoglu, Ender and Shotton, Jamie and Pathak, Sayan and White, Steve and Siddiqui, Khan},
  journal={Medical image analysis},
  volume={17},
  number={8},
  pages={1293--1303},
  year={2013},
  publisher={Elsevier}
}
% machine learning paper
@inproceedings{samarakoon2017light,
  title={Light Random Regression Forests for automatic multi-organ localization in CT images},
  author={Samarakoon, Prasad N and Promayon, Emmanuel and Fouard, C{\'e}line},
  booktitle={2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)},
  pages={371--374},
  year={2017},
  organization={IEEE}
}
% machine learning paper
@article{gauriau2015multi,
  title={Multi-organ localization with cascaded global-to-local regression and shape prior},
  author={Gauriau, Romane and Cuingnet, R{\'e}mi and Lesage, David and Bloch, Isabelle},
  journal={Medical image analysis},
  volume={23},
  number={1},
  pages={70--83},
  year={2015},
  publisher={Elsevier}
}

% machine learning paper
@inproceedings{pauly2011fast,
  title={Fast multiple organ detection and localization in whole-body MR Dixon sequences},
  author={Pauly, Olivier and Glocker, Ben and Criminisi, Antonio and Mateus, Diana and M{\"o}ller, Axel Martinez and Nekolla, Stephan and Navab, Nassir},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={239--247},
  year={2011},
  organization={Springer}
}
% machine learning
@article{zhou2012automatic,
  title={Automatic localization of solid organs on 3D CT images by a collaborative majority voting decision based on ensemble learning},
  author={Zhou, Xiangrong and Wang, Song and Chen, Huayue and Hara, Takeshi and Yokoyama, Ryujiro and Kanematsu, Masayuki and Fujita, Hiroshi},
  journal={Computerized Medical Imaging and Graphics},
  volume={36},
  number={4},
  pages={304--313},
  year={2012},
  publisher={Elsevier}
}
% machine learning paper
@inproceedings{zheng2009marginal,
  title={Marginal space learning for efficient detection of 2D/3D anatomical structures in medical images},
  author={Zheng, Yefeng and Georgescu, Bogdan and Comaniciu, Dorin},
  booktitle={International Conference on Information Processing in Medical Imaging},
  pages={411--422},
  year={2009},
  organization={Springer}
}

%% cnn 2d
@inproceedings{mamani2017organ,
  title={Organ detection in thorax abdomen CT using multi-label convolutional neural networks},
  author={Mamani, Gabriel Efrain Humpire and Setio, Arnaud Arindra Adiyoso and van Ginneken, Bram and Jacobs, Colin},
  booktitle={Medical Imaging 2017: Computer-Aided Diagnosis},
  volume={10134},
  pages={1013416},
  year={2017},
  organization={International Society for Optics and Photonics}
}
%% cnn 2d
@inproceedings{de20162d,
  title={2D image classification for 3D anatomy localization: employing deep convolutional neural networks},
  author={de Vos, Bob D and Wolterink, Jelmer M and de Jong, Pim A and Viergever, Max A and I{\v{s}}gum, Ivana},
  booktitle={Medical Imaging 2016: Image Processing},
  volume={9784},
  pages={97841Y},
  year={2016},
  organization={International Society for Optics and Photonics}
}

%% cnn 2d
@inproceedings{hussain2017segmentation,
  title={Segmentation-free kidney localization and volume estimation using aggregated orthogonal decision CNNS},
  author={Hussain, Mohammad Arafat and Amir-Khalili, Alborz and Hamarneh, Ghassan and Abugharbieh, Rafeef},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={612--620},
  year={2017},
  organization={Springer}
}

%cnn 2d organ localization
@article{de2017convnet,
  title={ConvNet-based localization of anatomical structures in 3-D medical images},
  author={de Vos, Bob D and Wolterink, Jelmer M and de Jong, Pim A and Leiner, Tim and Viergever, Max A and I{\v{s}}gum, Ivana},
  journal={IEEE transactions on medical imaging},
  volume={36},
  number={7},
  pages={1470--1481},
  year={2017},
  publisher={IEEE}
}
%cnn 2d organ localization
@article{humpire2018efficient,
  title={Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans},
  author={Humpire-Mamani, Gabriel Efrain and Setio, Arnaud Arindra Adiyoso and van Ginneken, Bram and Jacobs, Colin},
  journal={Physics in Medicine \& Biology},
  volume={63},
  number={8},
  pages={085003},
  year={2018},
  publisher={IOP Publishing}
}

% 3D Faster RCNN 
@article{xu2019efficient,
  title={Efficient Multiple Organ Localization in CT Image using 3D Region Proposal Network},
  author={Xu, Xuanang and Zhou, Fugen and Liu, Bo and Fu, Dongshan and Bai, Xiangzhi},
  journal={IEEE transactions on medical imaging},
  year={2019},
  publisher={IEEE}
}

% rl object detection
@inproceedings{caicedo2015active,
  title={Active object localization with deep reinforcement learning},
  author={Caicedo, Juan C and Lazebnik, Svetlana},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={2488--2496},
  year={2015}
}

% RL landmark detection
@article{ghesu2017multi,
  title={Multi-scale deep reinforcement learning for real-time 3D-landmark detection in CT scans},
  author={Ghesu, Florin-Cristian and Georgescu, Bogdan and Zheng, Yefeng and Grbic, Sasa and Maier, Andreas and Hornegger, Joachim and Comaniciu, Dorin},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={41},
  number={1},
  pages={176--189},
  year={2017},
  publisher={IEEE}
}
%landmark detection RL QNNs
@article{alansary2019evaluating,
  title={Evaluating reinforcement learning agents for anatomical landmark detection},
  author={Alansary, Amir and Oktay, Ozan and Li, Yuanwei and Le Folgoc, Loic and Hou, Benjamin and Vaillant, Ghislain and Kamnitsas, Konstantinos and Vlontzos, Athanasios and Glocker, Ben and Kainz, Bernhard and others},
  journal={Medical image analysis},
  volume={53},
  pages={156--164},
  year={2019},
  publisher={Elsevier}
}
%RL breast lesion detection
@inproceedings{maicas2017deep,
  title={Deep reinforcement learning for active breast lesion detection from DCE-MRI},
  author={Maicas, Gabriel and Carneiro, Gustavo and Bradley, Andrew P and Nascimento, Jacinto C and Reid, Ian},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={665--673},
  year={2017},
  organization={Springer}
}
% RL seed paper
@article{mnih2015human,
  title={Human-level control through deep reinforcement learning},
  author={Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A and Veness, Joel and Bellemare, Marc G and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K and Ostrovski, Georg and others},
  journal={Nature},
  volume={518},
  number={7540},
  pages={529},
  year={2015},
  publisher={Nature Publishing Group}
}
%segmentation net for liver lesion
@inproceedings{christ2016automatic,
  title={Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields},
  author={Christ, Patrick Ferdinand and Elshaer, Mohamed Ezzeldin A and Ettlinger, Florian and Tatavarty, Sunil and Bickel, Marc and Bilic, Patrick and Rempfler, Markus and Armbruster, Marco and Hofmann, Felix and D’Anastasi, Melvin and others},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={415--423},
  year={2016},
  organization={Springer}
}
%lesion detection paper
@article{yap2017automated,
  title={Automated breast ultrasound lesions detection using convolutional neural networks},
  author={Yap, Moi Hoon and Pons, Gerard and Mart{\'\i}, Joan and Ganau, Sergi and Sent{\'\i}s, Melcior and Zwiggelaar, Reyer and Davison, Adrian K and Mart{\'\i}, Robert},
  journal={IEEE journal of biomedical and health informatics},
  volume={22},
  number={4},
  pages={1218--1226},
  year={2017},
  publisher={IEEE}
}
% lesion detection PSMA
@article{rowe2016psma,
  title={PSMA-based [18 F] DCFPyL PET/CT is superior to conventional imaging for lesion detection in patients with metastatic prostate cancer},
  author={Rowe, Steven P and Macura, Katarzyna J and Mena, Esther and Blackford, Amanda L and Nadal, Rosa and Antonarakis, Emmanuel S and Eisenberger, Mario and Carducci, Michael and Fan, Hong and Dannals, Robert F and others},
  journal={Molecular Imaging and Biology},
  volume={18},
  number={3},
  pages={411--419},
  year={2016},
  publisher={Springer}
}
%tumor segmentation
@article{li2018h,
  title={H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes},
  author={Li, Xiaomeng and Chen, Hao and Qi, Xiaojuan and Dou, Qi and Fu, Chi-Wing and Heng, Pheng-Ann},
  journal={IEEE transactions on medical imaging},
  volume={37},
  number={12},
  pages={2663--2674},
  year={2018},
  publisher={IEEE}
}
%segmenation kidney
@article{torres2018kidney,
  title={Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review},
  author={Torres, Helena R and Queiros, Sandro and Morais, Pedro and Oliveira, Bruno and Fonseca, Jaime C and Vilaca, Joao L},
  journal={Computer methods and programs in biomedicine},
  volume={157},
  pages={49--67},
  year={2018},
  publisher={Elsevier}
}
% segmentation paper 
@article{navarro2019shape,
  title={Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation},
  author={Navarro, Fernando and Shit, Suprosanna and Ezhov, Ivan and Paetzold, Johannes and Gafita, Andrei and Peeken, Jan and Combs, Stephanie and Menze, Bjoern},
  journal={arXiv preprint arXiv:1908.05099},
  year={2019}
}

%faster rcnn
@inproceedings{ren2015faster,
  title={Faster r-cnn: Towards real-time object detection with region proposal networks},
  author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
  booktitle={Advances in neural information processing systems},
  pages={91--99},
  year={2015}
}

%double q-learning
@inproceedings{van2016deep,
  title={Deep reinforcement learning with double q-learning},
  author={Van Hasselt, Hado and Guez, Arthur and Silver, David},
  booktitle={Thirtieth AAAI conference on artificial intelligence},
  year={2016}
}

%dueling q-learning
@article{wang2015dueling,
  title={Dueling network architectures for deep reinforcement learning},
  author={Wang, Ziyu and Schaul, Tom and Hessel, Matteo and Van Hasselt, Hado and Lanctot, Marc and De Freitas, Nando},
  journal={arXiv preprint arXiv:1511.06581},
  year={2015}
}





