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000607553 1001_ $$0P:(DE-H253)PIP1096575$$aReichmann, Jakob$$b0$$eFirst author
000607553 245__ $$a3D imaging of SARS-CoV-2 infected hamster lungs by X-ray phase contrast tomography enables drug testing
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000607553 520__ $$aX-ray Phase Contrast Tomography (XPCT) based on wavefield propagation has been established as a high resolution three-dimensional (3D) imaging modality, suitable to reconstruct the intricate structure of soft tissues, and the corresponding pathological alterations. However, for biomedical research, more is needed than 3D visualisation and rendering of the cytoarchitecture in a few selected cases. First, the throughput needs to be increased to cover a statistically relevant number of samples. Second, the cytoarchitecture has to be quantified in terms of morphometric parameters, independent of visual impression. Third, dimensionality reduction and classification are required for identification of effects and interpretation of results. To address these challenges, we here design and implement a novel integrated and high throughput XPCT imaging and analysis workflow for 3D histology, pathohistology and drug testing. Our approach uses semi-automated data acquisition, reconstruction and statistical quantification. We demonstrate its capability for the example of lung pathohistology in Covid-19. Using a small animal model, different Covid-19 drug candidates are administered after infection and tested in view of restoration of the physiological cytoarchitecture, specifically the alveolar morphology. To this end, we then use morphometric parameter determination followed by a dimensionality reduction and classification based on optimal transport. This approach allows efficient discrimination between physiological and pathological lung structure, thereby providing quantitative insights into the pathological progression and partial recovery due to drug treatment. Finally, we stress that the XPCT image chain implemented here only used synchrotron radiation for validation, while the data used for analysis was recorded with laboratory CT radiation, more easily accessible for pre-clinical research. 
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000607553 7001_ $$0P:(DE-HGF)0$$aSarrazin, Clement$$b1
000607553 7001_ $$0P:(DE-HGF)0$$aSchmale, Sebastian$$b2
000607553 7001_ $$0P:(DE-HGF)0$$aBlaurock, Claudia$$b3
000607553 7001_ $$0P:(DE-HGF)0$$aBalkema-Buschmann, Anne$$b4
000607553 7001_ $$0P:(DE-HGF)0$$aSchmitzer, Bernhard$$b5$$eCorresponding author
000607553 7001_ $$0P:(DE-H253)PIP1007848$$aSalditt, Tim$$b6$$eCorresponding author
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000607553 999C5 $$1M Töpperwien$$2Crossref$$t3d virtual histology of neuronal tissue by propagation-based x-ray phase-contrast tomography. Göttingen Series in x-ray Physics$$uTöpperwien, M. 3d virtual histology of neuronal tissue by propagation-based x-ray phase-contrast tomography. Göttingen Series in x-ray Physics ((Göttingen University Press, Göttingen, 2018).$$y2018
000607553 999C5 $$1M Töpperwien$$2Crossref$$9-- missing cx lookup --$$a10.1073/pnas.1801678115$$p6940 -$$tProc. Natl. Acad. Sci. U.S.A.$$uTöpperwien, M., van der Meer, F., Stadelmann, C. & Salditt, T. Three-dimensional virtual histology of human cerebellum by X-ray phase-contrast tomography. Proc. Natl. Acad. Sci. U.S.A. 115, 6940–6945. https://doi.org/10.1073/pnas.1801678115 (2018).$$v115$$y2018
000607553 999C5 $$1M Reichardt$$2Crossref$$9-- missing cx lookup --$$a10.7554/eLife.71359$$p71359 -$$tElife$$uReichardt, M. et al. 3D virtual histopathology of cardiac tissue from Covid-19 patients based on phase-contrast X-ray tomography. Elife 10, 71359. https://doi.org/10.7554/eLife.71359 (2021).$$v10$$y2021
000607553 999C5 $$1C Westöö$$2Crossref$$9-- missing cx lookup --$$a10.1152/ajplung.00432.2020$$tAmerican Journal of Physiology-Lung Cellular and Molecular Physiology$$uWestöö, C. et al. Distinct types of plexiform lesions identified by synchrotron-based phase-contrast micro-CT. American Journal of Physiology-Lung Cellular and Molecular Physiologyhttps://doi.org/10.1152/ajplung.00432.2020 (2021).$$y2021
000607553 999C5 $$1A Svetlove$$2Crossref$$9-- missing cx lookup --$$a10.3389/fgstr.2023.1283052$$tFrontiers in Gastroenterology$$uSvetlove, A. et al. X-ray phase-contrast 3D virtual histology characterises complex tissue architecture in colorectal cancer. Frontiers in Gastroenterologyhttps://doi.org/10.3389/fgstr.2023.1283052 (2023).$$y2023
000607553 999C5 $$1M Romano$$2Crossref$$9-- missing cx lookup --$$a10.1016/j.ijrobp.2021.10.009$$p818 -$$tInt. J. Radiat. Oncol. Biol. Phys.$$uRomano, M. et al. X-ray phase contrast 3D virtual histology: evaluation of lung alterations after microbeam irradiation. Int. J. Radiat. Oncol. Biol. Phys. 112, 818–830. https://doi.org/10.1016/j.ijrobp.2021.10.009 (2022).$$v112$$y2022
000607553 999C5 $$1W Vågberg$$2Crossref$$9-- missing cx lookup --$$a10.1038/s41598-018-29344-3$$p11014 -$$tSci. Rep.$$uVågberg, W., Persson, J., Szekely, L. & Hertz, H. M. Cellular-resolution 3D virtual histology of human coronary arteries using x-ray phase tomography. Sci. Rep. 8, 11014. https://doi.org/10.1038/s41598-018-29344-3 (2018).$$v8$$y2018
000607553 999C5 $$1M Chourrout$$2Crossref$$9-- missing cx lookup --$$a10.1101/2021.03.25.436908$$tBiomed. Opt. Express$$uChourrout, M. et al. Brain virtual histology with X-ray phase-contrast tomography Part II: 3D morphologies of amyloid-beta plaques in Alzheimer’s disease models. Biomed. Opt. Expresshttps://doi.org/10.1101/2021.03.25.436908 (2021).$$y2021
000607553 999C5 $$1P Baran$$2Crossref$$9-- missing cx lookup --$$a10.1109/TMI.2018.2845905$$p2642 -$$tIEEE Trans. Med. Imaging$$uBaran, P. et al. High-resolution X-ray phase-contrast 3-D imaging of breast tissue specimens as a possible adjunct to histopathology. IEEE Trans. Med. Imaging 37, 2642–2650. https://doi.org/10.1109/TMI.2018.2845905 (2018).$$v37$$y2018
000607553 999C5 $$1DW Parsons$$2Crossref$$9-- missing cx lookup --$$a10.1111/j.1469-7580.2008.00950.x$$p217 -$$tJ. Anat.$$uParsons, D. W. et al. High-resolution visualization of airspace structures in intact mice via synchrotron phase-contrast X-ray imaging (PCXI). J. Anat. 213, 217–227. https://doi.org/10.1111/j.1469-7580.2008.00950.x (2008).$$v213$$y2008
000607553 999C5 $$1DW O’Connell$$2Crossref$$9-- missing cx lookup --$$a10.1088/1361-6560/ac934d$$tPhysics in Medicine & Biology$$uO’Connell, D. W. et al. Accurate measures of changes in regional lung air volumes from chest x-rays of small animals. Physics in Medicine & Biology 67, 205002. https://doi.org/10.1088/1361-6560/ac934d (2022).$$v67$$y2022
000607553 999C5 $$1E Borisova$$2Crossref$$9-- missing cx lookup --$$a10.1007/s00418-020-01868-8$$p215 -$$tHistochem. Cell Biol.$$uBorisova, E. et al. Micrometer-resolution X-ray tomographic full-volume reconstruction of an intact post-mortem juvenile rat lung. Histochem. Cell Biol. 155, 215–226. https://doi.org/10.1007/s00418-020-01868-8 (2021).$$v155$$y2021
000607553 999C5 $$1CS Stahr$$2Crossref$$9-- missing cx lookup --$$a10.1038/srep29438$$p29438 -$$tSci. Rep.$$uStahr, C. S. et al. Quantification of heterogeneity in lung disease with image-based pulmonary function testing. Sci. Rep. 6, 29438. https://doi.org/10.1038/srep29438 (2016).$$v6$$y2016
000607553 999C5 $$1AFT Leong$$2Crossref$$9-- missing cx lookup --$$a10.1364/BOE.5.004024$$p4024 -$$tBiomed. Opt. Express$$uLeong, A. F. T. et al. Real-time measurement of alveolar size and population using phase contrast x-ray imaging. Biomed. Opt. Express 5, 4024–4038. https://doi.org/10.1364/BOE.5.004024 (2014).$$v5$$y2014
000607553 999C5 $$1KS Morgan$$2Crossref$$9-- missing cx lookup --$$a10.1107/S1600577519014863$$p164 -$$tJ. Synchrotron Radiat.$$uMorgan, K. S. et al. Methods for dynamic synchrotron X-ray respiratory imaging in live animals. J. Synchrotron Radiat. 27, 164–175. https://doi.org/10.1107/S1600577519014863 (2020).$$v27$$y2020
000607553 999C5 $$1S Bayat$$2Crossref$$9-- missing cx lookup --$$a10.1016/j.ejmp.2020.10.001$$p22 -$$tPhysica Medica: European Journal of Medical Physics$$uBayat, S., Porra, L., Suortti, P. & Thomlinson, W. Functional lung imaging with synchrotron radiation: Methods and preclinical applications. Physica Medica: European Journal of Medical Physics 79, 22–35. https://doi.org/10.1016/j.ejmp.2020.10.001 (2020).$$v79$$y2020
000607553 999C5 $$1S Bayat$$2Crossref$$9-- missing cx lookup --$$a10.1183/13993003.congress-2022.1741$$tEur. Respir. J.$$uBayat, S., Cercos, J., Fardin, L., Perchiazzi, G. & Bravin, A. Pulmonary vascular biomechanics imaged with synchrotron phase contrast microtomography in live rats. Eur. Respir. J.https://doi.org/10.1183/13993003.congress-2022.1741 (2022).$$y2022
000607553 999C5 $$1K Shaker$$2Crossref$$9-- missing cx lookup --$$a10.1038/s42005-021-00760-8$$p259 -$$tCommunications Physics$$uShaker, K., Häggmark, I., Reichmann, J., Arsenian-Henriksson, M. & Hertz, H. M. Phase-contrast X-ray tomography resolves the terminal bronchioles in free-breathing mice. Communications Physics 4, 259. https://doi.org/10.1038/s42005-021-00760-8 (2021).$$v4$$y2021
000607553 999C5 $$1S Bayat$$2Crossref$$9-- missing cx lookup --$$a10.3389/fphys.2022.825433$$tFront. Physiol.$$uBayat, S., Fardin, L., Cercos-Pita, J. L., Perchiazzi, G. & Bravin, A. Imaging regional lung structure and function in small animals using synchrotron radiation phase-contrast and k-edge subtraction computed tomography. Front. Physiol. 13, 825433 (2022).$$v13$$y2022
000607553 999C5 $$1M Eckermann$$2Crossref$$9-- missing cx lookup --$$a10.7554/eLife.60408$$tElife$$uEckermann, M. et al. 3D virtual pathohistology of lung tissue from Covid-19 patients based on phase contrast X-ray tomography. Elife 9, e60408. https://doi.org/10.7554/eLife.60408 (2020).$$v9$$y2020
000607553 999C5 $$1JJ van Griethuysen$$2Crossref$$9-- missing cx lookup --$$a10.1158/0008-5472.CAN-17-0339$$pe104 -$$tCan. Res.$$uvan Griethuysen, J. J. et al. Computational Radiomics System to Decode the Radiographic Phenotype. Can. Res. 77, e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339 (2017).$$v77$$y2017
000607553 999C5 $$1G Lovric$$2Crossref$$9-- missing cx lookup --$$a10.1371/journal.pone.0183979$$tPLoS ONE$$uLovric, G. et al. Automated computer-assisted quantitative analysis of intact murine lungs at the alveolar scale. PLoS ONE 12, e0183979. https://doi.org/10.1371/journal.pone.0183979 (2017).$$v12$$y2017
000607553 999C5 $$1V Kumar$$2Crossref$$9-- missing cx lookup --$$a10.1016/j.mri.2012.06.010$$p1234 -$$tMagn. Reson. Imaging$$uKumar, V. et al. Radiomics: the process and the challenges. Magn. Reson. Imaging 30, 1234–1248. https://doi.org/10.1016/j.mri.2012.06.010 (2012).$$v30$$y2012
000607553 999C5 $$1P Lambin$$2Crossref$$9-- missing cx lookup --$$a10.1016/j.ejca.2011.11.036$$p441 -$$tEur. J. Cancer$$uLambin, P. et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446. https://doi.org/10.1016/j.ejca.2011.11.036 (2012).$$v48$$y2012
000607553 999C5 $$1G Peyré$$2Crossref$$9-- missing cx lookup --$$a10.1561/2200000073$$p355 -$$tFoundations and Trends in Machine Learning$$uPeyré, G. & Cuturi, M. Computational Optimal Transport. Foundations and Trends in Machine Learning 11(5–6), 355–602 (2019).$$v11$$y2019
000607553 999C5 $$1S Foxley$$2Crossref$$9-- missing cx lookup --$$a10.1016/j.neuroimage.2021.118250$$tNeuroimage$$uFoxley, S. et al. Multi-modal imaging of a single mouse brain over five orders of magnitude of resolution. Neuroimage 238, 118250. https://doi.org/10.1016/j.neuroimage.2021.118250 (2021).$$v238$$y2021
000607553 999C5 $$1S Haker$$2Crossref$$9-- missing cx lookup --$$a10.1023/B:VISI.0000036836.66311.97$$p225 -$$tInt. J. Comput. Vision$$uHaker, S., Zhu, L., Tannenbaum, A. & Angenent, S. Optimal Mass Transport for Registration and Warping. Int. J. Comput. Vision 60, 225–240. https://doi.org/10.1023/B:VISI.0000036836.66311.97 (2004).$$v60$$y2004
000607553 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1007/978-3-319-18461-6_21$$uRabin, J. & Papadakis, N. Convex color image segmentation with optimal transport distances (2015). ArXiv:1503.01986 [cs].
000607553 999C5 $$1Y Guo$$2Crossref$$9-- missing cx lookup --$$a10.1016/j.fmre.2023.06.006$$tFundamental Research$$uGuo, Y., Wang, X., Li, C. & Ying, S. Domain adaptive semantic segmentation by optimal transport. Fundamental Researchhttps://doi.org/10.1016/j.fmre.2023.06.006 (2023).$$y2023
000607553 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1007/978-3-642-40395-8_10$$uSchmitzer, B. & Schnörr, C. Object Segmentation by Shape Matching with Wasserstein Modes. In Heyden, A., Kahl, F., Olsson, C., Oskarsson, M. & Tai, X.-C. (eds.) Energy Minimization Methods in Computer Vision and Pattern Recognition, Lecture Notes in Computer Science, 123–136, https://doi.org/10.1007/978-3-642-40395-8_10 (Springer, Berlin, Heidelberg, 2013).
000607553 999C5 $$2Crossref$$uCrook, O. M. et al. A Linear Transportation Lp Distance for Pattern Recognition (2020). ArXiv:2009.11262 [cs, math].
000607553 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1109/IGARSS.2016.7729925$$uCourty, N., Flamary, R., Tuia, D. & Corpetti, T. Optimal transport for data fusion in remote sensing. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3571–3574,https://doi.org/10.1109/IGARSS.2016.7729925 (2016). ISSN: 2153-7003.
000607553 999C5 $$1C Muñoz-Fontela$$2Crossref$$9-- missing cx lookup --$$a10.1038/s41586-020-2787-6$$p509 -$$tNature$$uMuñoz-Fontela, C. et al. Animal models for COVID-19. Nature 586, 509–515. https://doi.org/10.1038/s41586-020-2787-6 (2020).$$v586$$y2020
000607553 999C5 $$1C Blaurock$$2Crossref$$9-- missing cx lookup --$$a10.1038/s41598-022-19222-4$$p15069 -$$tSci. Rep.$$uBlaurock, C. et al. Compellingly high SARS-CoV-2 susceptibility of Golden Syrian hamsters suggests multiple zoonotic infections of pet hamsters during the COVID-19 pandemic. Sci. Rep. 12, 15069. https://doi.org/10.1038/s41598-022-19222-4 (2022).$$v12$$y2022
000607553 999C5 $$1R Wölfel$$2Crossref$$9-- missing cx lookup --$$a10.1038/s41586-020-2196-x$$p465 -$$tNature$$uWölfel, R. et al. Virological assessment of hospitalized patients with COVID-2019. Nature 581, 465–469. https://doi.org/10.1038/s41586-020-2196-x (2020).$$v581$$y2020
000607553 999C5 $$1J Reichmann$$2Crossref$$9-- missing cx lookup --$$a10.1088/1361-6560/acd48d$$tPhysics in Medicine & Biology$$uReichmann, J. et al. Human lung virtual histology by multi-scale x-ray phase-contrast computed tomography. Physics in Medicine & Biology 68, 115014. https://doi.org/10.1088/1361-6560/acd48d (2023).$$v68$$y2023
000607553 999C5 $$1D Paganin$$2Crossref$$9-- missing cx lookup --$$a10.1046/j.1365-2818.2002.01010.x$$p33 -$$tJ. Microsc.$$uPaganin, D., Mayo, S. C., Gureyev, T. E., Miller, P. R. & Wilkins, S. W. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. J. Microsc. 206, 33–40. https://doi.org/10.1046/j.1365-2818.2002.01010.x (2002).$$v206$$y2002
000607553 999C5 $$1J Frohn$$2Crossref$$9-- missing cx lookup --$$a10.1107/S1600577520011327$$p1707 -$$tJ. Synchrotron Radiat.$$uFrohn, J. et al. 3D virtual histology of human pancreatic tissue by multiscale phase-contrast X-ray tomography. J. Synchrotron Radiat. 27, 1707–1719. https://doi.org/10.1107/S1600577520011327 (2020).$$v27$$y2020
000607553 999C5 $$1P Cloetens$$2Crossref$$9-- missing cx lookup --$$a10.1063/1.125225$$p2912 -$$tAppl. Phys. Lett.$$uCloetens, P. et al. Holotomography: Quantitative phase tomography with micrometer resolution using hard synchrotron radiation x rays. Appl. Phys. Lett. 75, 2912–2914. https://doi.org/10.1063/1.125225 (1999).$$v75$$y1999
000607553 999C5 $$1L Lohse$$2Crossref$$9-- missing cx lookup --$$a10.1107/S1600577520002398$$tJ. Synchrotron Radiat.$$uLohse, L. et al. A phase-retrieval toolbox for X-ray holography and tomography. J. Synchrotron Radiat.https://doi.org/10.1107/S1600577520002398 (2020).$$y2020
000607553 999C5 $$1B Yu$$2Crossref$$9-- missing cx lookup --$$a10.1364/OE.26.011110$$p11110 -$$tOpt. Express$$uYu, B. et al. Evaluation of phase retrieval approaches in magnified X-ray phase nano computerized tomography applied to bone tissue. Opt. Express 26, 11110–11124. https://doi.org/10.1364/OE.26.011110 (2018).$$v26$$y2018
000607553 999C5 $$1W van Aarle$$2Crossref$$9-- missing cx lookup --$$a10.1016/j.ultramic.2015.05.002$$p35 -$$tUltramicroscopy$$uvan Aarle, W. et al. The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography. Ultramicroscopy 157, 35–47. https://doi.org/10.1016/j.ultramic.2015.05.002 (2015).$$v157$$y2015
000607553 999C5 $$1A Mirone$$2Crossref$$9-- missing cx lookup --$$a10.1016/j.nimb.2013.09.030$$p41 -$$tNucl. Instrum. Methods Phys. Res., Sect. B$$uMirone, A., Brun, E., Gouillart, E., Tafforeau, P. & Kieffer, J. The PyHST2 hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities. Nucl. Instrum. Methods Phys. Res., Sect. B 324, 41–48. https://doi.org/10.1016/j.nimb.2013.09.030 (2014).$$v324$$y2014
000607553 999C5 $$1L Knudsen$$2Crossref$$9-- missing cx lookup --$$a10.1152/japplphysiol.01100.2009$$p412 -$$tJ. Appl. Physiol.$$uKnudsen, L., Weibel, E. R., Gundersen, H. J. G., Weinstein, F. V. & Ochs, M. Assessment of air space size characteristics by intercept (chord) measurement: An accurate and efficient stereological approach. J. Appl. Physiol. 108, 412–421. https://doi.org/10.1152/japplphysiol.01100.2009 (2010).$$v108$$y2010
000607553 999C5 $$1MR MacIver$$2Crossref$$9-- missing cx lookup --$$a10.1002/ceat.201600523$$p2305 -$$tChemical Engineering & Technology$$uMacIver, M. R. & Pawlik, M. Analysis of in situ microscopy images of flocculated sediment volumes. Chemical Engineering & Technology 40, 2305–2313. https://doi.org/10.1002/ceat.201600523 (2017).$$v40$$y2017
000607553 999C5 $$1B Lu$$2Crossref$$9-- missing cx lookup --$$a10.1063/1.464812$$p6472 -$$tJ. Chem. Phys.$$uLu, B. & Torquato, S. Chord-length and free-path distribution functions for many-body systems. J. Chem. Phys. 98, 6472–6482. https://doi.org/10.1063/1.464812 (1993).$$v98$$y1993
000607553 999C5 $$1N Otsu$$2Crossref$$9-- missing cx lookup --$$a10.1109/TSMC.1979.4310076$$p62 -$$tIEEE Trans. Syst. Man Cybern.$$uOtsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66. https://doi.org/10.1109/TSMC.1979.4310076 (1979).$$v9$$y1979
000607553 999C5 $$1JE Bresenham$$2Crossref$$9-- missing cx lookup --$$a10.1147/sj.41.0025$$p25 -$$tIBM Syst. J.$$uBresenham, J. E. Algorithm for computer control of a digital plotter. IBM Syst. J. 4, 25–30. https://doi.org/10.1147/sj.41.0025 (1965).$$v4$$y1965
000607553 999C5 $$2Crossref$$uMacIver, M. R. Chord Length Distribution from Binary 2D Images (2023).
000607553 999C5 $$1S-Y Chung$$2Crossref$$9-- missing cx lookup --$$a10.1016/j.matchar.2020.110182$$tMater. Charact.$$uChung, S.-Y., Sikora, P., Rucińska, T., Stephan, D. & Abd Elrahman, M. Comparison of the pore size distributions of concretes with different air-entraining admixture dosages using 2D and 3D imaging approaches. Mater. Charact. 162, 110182. https://doi.org/10.1016/j.matchar.2020.110182 (2020).$$v162$$y2020
000607553 999C5 $$1DM Turner$$2Crossref$$9-- missing cx lookup --$$a10.1088/0965-0393/24/7/075002$$tModell. Simul. Mater. Sci. Eng.$$uTurner, D. M., Niezgoda, S. R. & Kalidindi, S. R. Efficient computation of the angularly resolved chord length distributions and lineal path functions in large microstructure datasets. Modell. Simul. Mater. Sci. Eng. 24, 075002. https://doi.org/10.1088/0965-0393/24/7/075002 (2016).$$v24$$y2016
000607553 999C5 $$1MI Latypov$$2Crossref$$9-- missing cx lookup --$$a10.1016/j.matchar.2018.09.020$$p671 -$$tMater. Charact.$$uLatypov, M. I. et al. Application of chord length distributions and principal component analysis for quantification and representation of diverse polycrystalline microstructures. Mater. Charact. 145, 671–685. https://doi.org/10.1016/j.matchar.2018.09.020 (2018).$$v145$$y2018
000607553 999C5 $$1G Crowley$$2Crossref$$9-- missing cx lookup --$$a10.1186/s12890-019-0915-6$$p206 -$$tBMC Pulm. Med.$$uCrowley, G. et al. Quantitative lung morphology: semi-automated measurement of mean linear intercept. BMC Pulm. Med. 19, 206. https://doi.org/10.1186/s12890-019-0915-6 (2019).$$v19$$y2019
000607553 999C5 $$1F Santambrogio$$2Crossref$$9-- missing cx lookup --$$a10.1007/978-3-319-20828-2$$uSantambrogio, F. Optimal Transport for Applied Mathematicians: Calculus of Variations, PDEs, and Modeling (Birkhäuser (Google-Books-ID, UOHHCgAAQBAJ, 2015).$$y2015
000607553 999C5 $$1S Kolouri$$2Crossref$$9-- missing cx lookup --$$a10.1109/MSP.2017.2695801$$p43 -$$tIEEE Signal Process. Mag.$$uKolouri, S., Park, S. R., Thorpe, M., Slepcev, D. & Rohde, G. K. Optimal Mass Transport: Signal processing and machine-learning applications. IEEE Signal Process. Mag. 34, 43–59. https://doi.org/10.1109/MSP.2017.2695801 (2017).$$v34$$y2017
000607553 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1109/CVPR.2018.00820$$uPark, S. & Thorpe, M. Representing and Learning High Dimensional Data with the Optimal Transport Map from a Probabilistic Viewpoint. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7864–7872, https://doi.org/10.1109/CVPR.2018.00820 (IEEE, Salt Lake City, UT, 2018).
000607553 999C5 $$1P Virtanen$$2Crossref$$9-- missing cx lookup --$$a10.1038/s41592-019-0686-2$$p261 -$$tNat. Methods$$uVirtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272. https://doi.org/10.1038/s41592-019-0686-2 (2020).$$v17$$y2020
000607553 999C5 $$1W Wang$$2Crossref$$9-- missing cx lookup --$$a10.1007/s11263-012-0566-z$$p254 -$$tInt. J. Comput. Vision$$uWang, W., Slepčev, D., Basu, S., Ozolek, J. A. & Rohde, G. K. A linear optimal transportation framework for quantifying and visualizing variations in sets of images. Int. J. Comput. Vision 101, 254–269. https://doi.org/10.1007/s11263-012-0566-z (2013).$$v101$$y2013
000607553 999C5 $$1J Frost$$2Crossref$$9-- missing cx lookup --$$a10.1101/2022.10.07.22280811$$tPathology$$uFrost, J. et al. 3d virtual histology reveals pathological alterations of cerebellar granule cells in multiple sclerosis. Pathologyhttps://doi.org/10.1101/2022.10.07.22280811 (2023).$$y2023
000607553 999C5 $$1AR Kennedy$$2Crossref$$uKennedy, A. R., Desrosiers, A., Terzaghi, M. & Little, J. B. Morphometric and histological analysis of the lungs of Syrian golden hamsters. J. Anat. 125, 527–553 (1978).$$y1978
000607553 999C5 $$1C Cortes$$2Crossref$$9-- missing cx lookup --$$a10.1007/BF00994018$$p273 -$$tMach. Learn.$$uCortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297. https://doi.org/10.1007/BF00994018 (1995).$$v20$$y1995