
@article{zhang_anatomically_2018,
	title = {An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan},
	volume = {179},
	journal = {NeuroImage},
	author = {Zhang, Fan and Wu, Ye and Norton, Isaiah and Rigolo, Laura and Rathi, Yogesh and Makris, Nikos and O'Donnell, Lauren J.},
	year = {2018},
	pages = {429--447}
}

@inproceedings{feydy2019interpolating,
  title={Interpolating between {Optimal} {Transport} and {MMD} using {Sinkhorn} divergences},
  author={Feydy, Jean and S{\'e}journ{\'e}, Thibault and Vialard, Fran{\c{c}}ois-Xavier and Amari, Shun-Ichi and Trouv{\'e}, Alain and Peyr{\'e}, Gabriel},
  booktitle={AiStats},
  year={2019}
}


@inproceedings{feydy2018global,
  title={Global divergences between measures: from {Hausdorff} distance to {Optimal} {Transport}},
  author={Feydy, Jean and Trouv{\'e}, Alain},
  booktitle={ShapeMI, MICCAI workshop},
  pages={102--115},
  year={2018}
  }

@misc{keops,
	title = {Kernel {Operations} on the {GPU}, with autodiff, without memory overflows},
	url = {https://www.kernel-operations.io/},
	urldate = {2019-03-18},
	author={Charlier, Benjamin and Feydy, Jean and Glaunès, Joan}
}

@article{chui2003new,
  title={A new point matching algorithm for non-rigid registration},
  author={Chui, Haili and Rangarajan, Anand},
  journal={Computer Vision and Image Understanding},
  volume={89},
  number={2-3},
  pages={114--141},
  year={2003},
  publisher={Elsevier}
}

@article{kosowsky1994invisible,
  title={The invisible hand algorithm: {Solving} the assignment problem with statistical physics},
  author={Kosowsky, JJ and Yuille, Alan L},
  journal={Neural networks},
  volume={7},
  number={3},
  pages={477--490},
  year={1994},
  publisher={Elsevier}
}

@inproceedings{cuturi2013sinkhorn,
  title={{Sinkhorn} distances: {Lightspeed} computation of optimal transport},
  author={Cuturi, Marco},
  booktitle={NIPS},
  pages={2292--2300},
  year={2013}
}

@article{peyre2019computational,
  title={Computational optimal transport},
  author={Peyr{\'e}, Gabriel and Cuturi, Marco and others},
  journal={Foundations and Trends in Machine Learning},
  volume={11},
  number={5-6},
  pages={355--607},
  year={2019},
  publisher={Now Publishers, Inc.}
}

@article{ramdas2017wasserstein,
  title={On wasserstein two-sample testing and related families of nonparametric tests},
  author={Ramdas, Aaditya and Trillos, Nicol{\'a}s and Cuturi, Marco},
  journal={Entropy},
  volume={19},
  number={2},
  pages={47},
  year={2017},
  publisher={Multidisciplinary Digital Publishing Institute}
}

@inproceedings{genevay2017learning,
  title={Learning generative models with sinkhorn divergences},
  author={Genevay, Aude and Peyr{\'e}, Gabriel and Cuturi, Marco},
  journal={AiStats},
  year={2018}
}

@article{schmitzer2016stabilized,
  title={Stabilized sparse scaling algorithms for entropy regularized transport problems},
  author={Schmitzer, Bernhard},
  journal={arXiv preprint arXiv:1610.06519},
  year={2016}
}

@inproceedings{gerber2018exploratory,
  title={Exploratory Population Analysis with Unbalanced Optimal Transport},
  author={Gerber, Samuel and Niethammer, Marc and Styner, Martin and Aylward, Stephen},
  booktitle={MICCAI},
  pages={464--472},
  year={2018}
  }


@article{chizat2018interpolating,
  title={An interpolating distance between optimal transport and {Fisher}--{Rao} metrics},
  author={Chizat, Lenaic and Peyr{\'e}, Gabriel and Schmitzer, Bernhard and Vialard, Fran{\c{c}}ois-Xavier},
  journal={Foundations of Computational Mathematics},
  volume={18},
  number={1},
  pages={1--44},
  year={2018},
  publisher={Springer}
}

@article{liero2018optimal,
  title={Optimal entropy-transport problems and a new Hellinger--Kantorovich distance between positive measures},
  author={Liero, Matthias and Mielke, Alexander and Savar{\'e}, Giuseppe},
  journal={Inventiones mathematicae},
  volume={211},
  number={3},
  pages={969--1117},
  year={2018},
  publisher={Springer}
}

@article{leonard2013survey,
  title={A survey of the {Schr\"{o}dinger} problem and some of its connections with optimal transport},
  author={L{\'e}onard, Christian},
  journal={arXiv preprint arXiv:1308.0215},
  year={2013}
}


@inproceedings{delmonte_white_2019,
	address = {Venice, Italy},
	title = {White matter multi-resolution segmentation using fuzzy set theory},
	booktitle = {{IEEE} {ISBI}},
	author = {Delmonte, Alessandro and Mercier, Corentin and Pallud, Johan and Bloch, Isabelle and Gori, Pietro},
	year = {2019}
}

@article{sharmin_white_2018,
	title = {White {Matter} {Tract} {Segmentation} as {Multiple} {Linear} {Assignment} {Problems}},
	volume = {11},
	journal = {Frontiers in Neuroscience},
	author = {Sharmin, Nusrat and Olivetti, Emanuele and Avesani, Paolo},
	year = {2018}
}

@article{olivetti_alignment_2016,
	title = {Alignment of {Tractograms} {As} {Graph} {Matching}},
	volume = {10},
	issn = {1662-453X},
	url = {https://www.frontiersin.org/articles/10.3389/fnins.2016.00554/full},
	doi = {10.3389/fnins.2016.00554},
	abstract = {The white matter pathways of the brain can be reconstructed as 3D polylines, called streamlines, through the analysis of diffusion magnetic resonance imaging (dMRI) data. The whole set of streamlines is called tractogram and represents the structural connectome of the brain. In multiple applications, like group-analysis, segmentation or atlasing, tractograms of different subjects need to be aligned. Typically, this is done with registration methods, that transform the tractograms in order to increase their similarity. In contrast with transformation-based registration methods, in this work we propose the concept of tractogram correspondence, whose aim is to find which streamline of one tractogram corresponds to which streamline in another tractogram, i.e. a map from one tractogram to another. As a further contribution, we propose to use the relational information of each streamline, i.e. its distances from the other streamlines in its own tractogram, as the building block to define the optimal correspondence. We provide an operational procedure to find the optimal correspondence through a combinatorial optimization problem and we discuss its similarity to the graph matching problem. In this work, we propose to represent tractograms as graphs and we adopt a recent inexact sub-graph matching algorithm to approximate the solution of the tractogram correspondence problem. On tractograms generated from the Human Connectome Project dataset, we report experimental evidence that tractogram correspondence, implemented as graph matching, provides much better alignment than affine registration and comparable if not better results than nonlinear registration of volumes.},
	language = {English},
	urldate = {2019-03-19},
	journal = {Frontiers in Neuroscience},
	author = {Olivetti, Emanuele and Sharmin, Nusrat and Avesani, Paolo},
	year = {2016},
	keywords = {tractography, diffusion MRI, Alignment, combinatorial optimization, Graph matching},
	file = {Full Text PDF:/Users/pgori/Zotero/storage/QAR7C8I7/Olivetti et al. - 2016 - Alignment of Tractograms As Graph Matching.pdf:application/pdf}
}

@article{zhang_anatomically_2018,
	title = {An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan},
	volume = {179},
	journal = {NeuroImage},
	author = {Zhang, Fan and Wu, Ye and Norton, Isaiah and Rigolo, Laura and Rathi, Yogesh and Makris, Nikos and O'Donnell, Lauren J.},
	year = {2018},
	pages = {429--447}
}



@misc{noauthor_kernel_nodate,
	title = {Kernel {Operations} on the {GPU}, with autodiff, without memory overflows — {KeOps}},
	url = {https://www.kernel-operations.io/keops/index.html},
	urldate = {2019-03-18},
	file = {Kernel Operations on the GPU, with autodiff, without memory overflows — KeOps:/Users/pgori/Zotero/storage/R34T7CY3/index.html:text/html}
}

@article{hua_tract_2008,
	title = {Tract probability maps in stereotaxic spaces: {Analyses} of white matter anatomy and tract-specific quantification},
	volume = {39},
	number = {1},
	journal = {NeuroImage},
	author = {Hua, Kegang and Zhang, Jiangyang and Wakana, Setsu and Jiang, Hangyi and Li, Xin and Reich, Daniel S. and Calabresi, Peter A. and Pekar, James J. and van Zijl, Peter C. M. and Mori, Susumu},
	year = {2008},
	pages = {336--347}
}

@article{wasserthal_tractseg_2018,
	title = {{TractSeg} - {Fast} and accurate white matter tract segmentation},
	volume = {183},
	journal = {NeuroImage},
	author = {Wasserthal, Jakob and Neher, Peter and Maier-Hein, Klaus H.},
	year = {2018},
	pages = {239--253}
}

@article{siless_anatomicuts:_2018,
	title = {{AnatomiCuts}: {Hierarchical} clustering of tractography streamlines based on anatomical similarity},
	volume = {166},
	issn = {1053-8119},
	shorttitle = {{AnatomiCuts}},
	url = {http://www.sciencedirect.com/science/article/pii/S1053811917308923},
	doi = {10.1016/j.neuroimage.2017.10.058},
	abstract = {Diffusion MRI tractography produces massive sets of streamlines that contain a wealth of information on brain connections. The size of these datasets creates a need for automated clustering methods to group the streamlines into meaningful bundles. Conventional clustering techniques group streamlines based on their spatial coordinates. Neuroanatomists, however, define white-matter bundles based on the anatomical structures that they go through or next to, rather than their spatial coordinates. Thus we propose a similarity measure for clustering streamlines based on their position relative to cortical and subcortical brain regions. We incorporate this measure into a hierarchical clustering algorithm and compare it to a measure that relies on Euclidean distance, using data from the Human Connectome Project. We show that the anatomical similarity measure leads to a 20\% improvement in the overlap of clusters with manually labeled tracts. Importantly, this is achieved without introducing any prior information from a tract atlas into the clustering algorithm, therefore without imposing the existence of any named tracts.},
	urldate = {2019-04-01},
	journal = {NeuroImage},
	author = {Siless, Viviana and Chang, Ken and Fischl, Bruce and Yendiki, Anastasia},
	month = feb,
	year = {2018},
	keywords = {Diffusion MRI, Hierarchical clustering, Normalized cuts, Tractography},
	pages = {32--45},
	file = {ScienceDirect Full Text PDF:/Users/pgori/Zotero/storage/J62H3CT5/Siless et al. - 2018 - AnatomiCuts Hierarchical clustering of tractograp.pdf:application/pdf;ScienceDirect Snapshot:/Users/pgori/Zotero/storage/E7UDQTJN/S1053811917308923.html:text/html}
}

@article{garyfallidis_recognition_2018,
	series = {Segmenting the {Brain}},
	title = {Recognition of white matter bundles using local and global streamline-based registration and clustering},
	volume = {170},
	journal = {NeuroImage},
	author = {Garyfallidis, Eleftherios and Côté, Marc-Alexandre and Rheault, Francois and Sidhu, Jasmeen and Hau, Janice and others},
	year = {2018},
	pages = {283--295}
}


@article{wassermann_white_2016,
	title = {The white matter query language: a novel approach for describing human white matter anatomy},
	volume = {221},
	number = {9},
	urldate = {2019-04-01},
	journal = {Brain Structure and Function},
	author = {Wassermann, Demian and Makris, Nikos and Rathi, Yogesh and Shenton, Martha and Kikinis, Ron and Kubicki, Marek and Westin, Carl-Fredrik},
	year = {2016},
	pages = {4705--4721}
}

@article{guevara_automatic_2012,
	title = {Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas},
	volume = {61},
	journal = {NeuroImage},
	author = {Guevara, P. and Duclap, D. and Poupon, C. and Marrakchi-Kacem, L. and Fillard, P. and Le Bihan, D. and Leboyer, M. and Houenou, J. and Mangin, J. -F.},
	year = {2012},
	pages = {1083--1099}
}


@article{wassermann_unsupervised_2010,
	title = {Unsupervised white matter fiber clustering and tract probability map generation},
	volume = {51},
	number = {1},
	urldate = {2019-04-01},
	journal = {NeuroImage},
	author = {Wassermann, D. and Bloy, L. and Kanterakis, E. and Verma, R. and Deriche, R.},
	year = {2010},
	pages = {228--241}
}



@article{wakana_reproducibility_2007,
	title = {Reproducibility of quantitative tractography methods applied to cerebral white matter},
	volume = {36},
	number = {3},
	journal = {NeuroImage},
	author = {Wakana, Setsu and Caprihan, Arvind and Panzenboeck, Martina M. and Fallon, James H. and Perry, Michele and Gollub, Randy L. and Hua, Kegang and Zhang, Jiangyang and Jiang, Hangyi and Dubey, Prachi and Blitz, Ari and van Zijl, Peter and Mori, Susumu},
	year = {2007},
	pages = {630--644}
}