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We present recent highlights from the most recent operation phases of Wendelstein 7-X, the most advanced stellarator in the world. Stable detachment with good particle exhaust, low impurity content, and energy confinement times exceeding 100 ms, have been maintained for tens of seconds. Pellet fueling allows for plasma phases with reduced ion-temperature-gradient turbulence, and during such phases, the overall confinement is so good (energy confinement times often exceeding 200 ms) that the attained density and temperature profiles would not have been possible in less optimized devices, since they would have had neoclassical transport losses exceeding the heating applied in W7-X. This provides proof that the reduction of neoclassical transport through magnetic field optimization is successful. W7-X plasmas generally show good impurity screening and high plasma purity, but there is evidence of longer impurity confinement times during turbulence-suppressed phases. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 and 2019-2020 under Grant Agreement No. 633053. Peer Reviewed Article signat per 497 autors/es: Thomas Sunn Pedersen1,2,∗ , I. Abramovic3, P. Agostinetti4, M. Agredano Torres1, S. Äkäslompolo1, J. Alcuson Belloso1, P. Aleynikov1, K. Aleynikova1, M. Alhashimi1, A. Ali1, N. Allen5, A. Alonso6, G. Anda7, T. Andreeva1, C. Angioni8, A. Arkhipov8, A. Arnold1, W. Asad8, E. Ascasibar6, M.-H. Aumeunier9, K. Avramidis10, E. Aymerich11, S.-G. Baek3, J. Bähner1, A. Baillod12, M. Balden1, M. Balden8, J. Baldzuhn1, S. Ballinger3, M. Banduch1, S. Bannmann1, A. Banon Navarro8, A. Bañon Navarro ´ 1, T. Barbui13, C. Beidler1, C. Belafdil9, A. Bencze7, A. Benndorf1, M. Beurskens1, C. Biedermann1, O. Biletskyi14, B. Blackwell15, M. Blatzheim1, T. Bluhm1, D. Böckenhoff1, G. Bongiovi16, M. Borchardt1, D. Borodin17, J. Boscary8, H. Bosch1,18, T. Bosmann19, B. Böswirth8, L. Böttger1, A. Bottino8, S. Bozhenkov1, R. Brakel1, C. Brandt1, T. Bräuer1, H. Braune1, S. Brezinsek17, K. Brunner1, S. Buller1, R. Burhenn1, R. Bussiahn1, B. Buttenschön1, A. Buzás7, V. Bykov1, I. Calvo6, K. Camacho Mata1, I. Caminal20, B. Cannas11, A. Cappa6, A. Carls1, F. Carovani1, M. Carr21, D. Carralero6, B. Carvalho22, J. Casas20, D. Castano-Bardawil17, F. Castejon6, N. Chaudhary1, I. Chelis23, A. Chomiczewska24, J.W. Coenen13,17, M. Cole1, F. Cordella25, Y. Corre9, K. Crombe26, G. Cseh7, B. Csillag7, H. Damm1, C. Day10, M. de Baar27, E. De la Cal6, S. Degenkolbe1, A. Demby13, S. Denk3, C. Dhard1, A. Di Siena8,28, A. Dinklage12, T. Dittmar17, M. Dreval14, M. Drevlak1, P. Drewelow1, P. Drews17, D. Dunai7, E. Edlund3, F. Effenberg29, G. Ehrke1, M. Endler1, D.A. Ennis5, F.J. Escoto6, T. Estrada6, E. Fable8, N. Fahrenkamp1, A. Fanni11, J. Faustin1, J. Fellinger1, Y. Feng1, W. Figacz4, E. Flom13, O. Ford1, T. Fornal24, H. Frerichs13, S. Freundt1, G. Fuchert1, M. Fukuyama30, F. Füllenbach1, G. Gantenbein10, Y. Gao1, K. Garcia13, J.M. García Regaña6, I. García-Cortés6, J. Gaspar31, D.A. Gates29, J. Geiger1, B. Geiger13, L. Giudicotti32, A. González6, A. Goriaev26,33, D. Gradic1, M. Grahl1, J.P. Graves12, J. Green13, E. Grelier9, H. Greuner8, S. Groß1, H. Grote1, M. Groth34, M. Gruca24, O. Grulke1,35, M. Grün1, J. Guerrero Arnaiz1, S. Günter8, V. Haak1, M. Haas1, P. Hacker1, A. Hakola36, A. Hallenbert1, K. Hammond29, X. Han17,37, S.K. Hansen3, J.H. Harris38, H. Hartfuß1, D. Hartmann1, D. Hathiramani1, R. Hatzky8, J. Hawke39, S. Hegedus7, B. Hein8, B. Heinemann8, P. Helander12, S. Henneberg1, U. Hergenhahn8,40, C. Hidalgo6, F. Hindenlang8, M. Hirsch1, U. Höfel1, K.P. Hollfeld17, A. Holtz1, D. Hopf8, D. Höschen17, M. Houry9, J. Howard19, X. Huang41, M. Hubeny17, S. Hudson29, K. Ida9, Y. Igitkhanov10, V. Igochine8, S. Illy10, C. Ionita-Schrittwieser42, M. Isobe39, M. Jabłczynska ´ 24, S. Jablonski24, B. Jagielski1, M. Jakubowski1, A. Jansen van Vuuren1, J. Jelonnek10, F. Jenko8, F. Jenko8, T. Jensen35, H. Jenzsch1, P. Junghanns8, J. Kaczmarczyk24, J. Kallmeyer1, U. Kamionka1, M. Kandler8, S. Kasilov43, Y. Kazakov26, D. Kennedy1, A. Kharwandikar1, M. Khokhlov1, C. Kiefer8, C. Killer1, A. Kirschner17, R. Kleiber1, T. Klinger12, S. Klose1, J. Knauer1, A. Knieps17, F. Köchl44, G. Kocsis7, Ya.I. Kolesnichenko45, A. Könies1, R. König1, J. Kontula34, P. Kornejew1, J. Koschinsky, M.M. Kozulia14, A. Krämer-Flecken17, R. Krampitz1, M. Krause1, N. Krawczyk24, T. Kremeyer1, L. Krier10, D.M. Kriete5, M. Krychowiak1, I. Ksiazek46, M. Kubkowska24, M. Kuczynski1, G. Kühner1, A. Kumar15, T. Kurki-Suonio34, S. Kwak1, M. Landreman47, P.T. Lang8, A. Langenberg1, H.P. Laqua12, H. Laqua1, R. Laube1, S. Lazerson1, M. Lewerentz1, C. Li17, Y. Liang17, Ch. Linsmeier17, J. Lion1, A. Litnovsky17,48, S. Liu37, J. Lobsien1, J. Loizu12, J. Lore38, A. Lorenz1, U. Losada6, F. Louche26, R. Lunsford29, V. Lutsenko45, M. Machielsen12, F. Mackel8, J. Maisano-Brown3, O. Maj8, D. Makowski49, G. Manduchi50, E. Maragkoudakis6, O. Marchuk17, S. Marsen1, E. Martines4, J. Martinez-Fernandez6, M. Marushchenko1, S. Masuzaki41, D. Maurer5, M. Mayer8, K.J. McCarthy6, O. Mccormack4, P. McNeely1, H. Meister8, B. Mendelevitch8, S. Mendes1, A. Merlo1, A. Messian26, A. Mielczarek49, O. Mishchenko1, B. Missal1, R. Mitteau9, V.E. Moiseenko14, A. Mollen1, V. Moncada9, T. Mönnich1, T. Morisaki41, D. Moseev1, G. Motojima41, S. Mulas6, M. Mulsow1, M. Nagel1, D. Naujoks1, V. Naulin35, T. Neelis19, H. Neilson29, R. Neu8, O. Neubauer17, U. Neuner1, D. Nicolai17, S.K. Nielsen35, H. Niemann1, T. Nishiza1, T. Nishizawa1, T. Nishizawa8, C. Nührenberg1, R. Ochoukov8, J. Oelmann17, G. Offermanns17 K. Ogawa41, S. Okamura41, J. Ölmanns17, J. Ongena26, J. Oosterbeek1, M. Otte1, N. Pablant29, N. Panadero Alvarez6, N. Panadero Alvarez6, A. Pandey1, E. Pasch1, R. Pavlichenko14, A. Pavone1, E. Pawelec46, G. Pechstein1, G. Pelka24, V. Perseo1, B. Peterson41, D. Pilopp1, S. Pingel1, F. Pisano11, B. Plöckl8, G. Plunk1, P. Pölöskei1, B. Pompe2, A. Popov51, M. Porkolab3, J. Proll19, M.J. Pueschel19,27, M.-E. Puiatti52, A. Puig Sitjes1, F. Purps1, K. Rahbarnia1, M. Rasinski ´ 17, J. Rasmussen35, A. Reiman29, F. Reimold1, M. Reisner8, D. Reiter17, M. Richou9, R. Riedl8, J. Riemann1, K. Riße1, G. Roberg-Clark1, V. Rohde8, J. Romazanov17, D. Rondeshagen1, P. Rong1, L. Rudischhauser1, T. Rummel1, K. Rummel1, A. Runov1, N. Rust1, L. Ryc24, P. Salembier20, M. Salewski35, E. Sanchez6, S. Satake41, G. Satheeswaran17, J. Schacht1, E. Scharff1, F. Schauer8, J. Schilling1, G. Schlisio1, K. Schmid8, J. Schmitt5, O. Schmitz13, W. Schneider1, M. Schneider1, P. Schneider8, R. Schrittwieser42, T. Schröder1, M. Schröder1, R. Schroeder1, B. Schweer26, D. Schwörer1, E. Scott1, E. Scott8, B. Shanahan1, G. Sias11, P. Sichta29, M. Singer1, P. Sinha29, S. Sipliä34, C. Slaby1, M. Sleczka53, H. Smith1, J. Smoniewski54, E. Sonnendrücker8, M. Spolaore4, A. Spring1, R. Stadler8, D. Stanczak24, T. Stange1, I. Stepanov26, L. Stephey13, J. Stober8, U. Stroth8,55, E. Strumberger8, C. Suzuki41, Y. Suzuki41, J. Svensson1, T. Szabolics7, T. Szepesi7, M. Szücs7, F.L. Tabares6, N. Tamura41, A. Tancetti35, C. Tantos10, J. Terry3, H. Thienpondt6, H. Thomsen1, M. Thumm10, J.M. Travere9, P. Traverso5, J. Tretter8, E. Trier8, H. Trimino Mora1, T. Tsujimura41, Y. Turkin1, A. Tykhyi45, B. Unterberg17, P. van Eeten1, B.Ph. van Milligen6, M. van Schoor26, L. Vano1, S. Varoutis10, M. Vecsei7, L. Vela56, J.L. Velasco6, M. Vervier17, N. Vianello50, H. Viebke1, R. Vilbrandt1, G. Vogel8, N. Vogt1, C. Volkhausen1, A. von Stechow1, F. Wagner1, E. Wang17, H. Wang57, F. Warmer1, T. Wauters26, L. Wegener1, T. Wegner1, G. Weir1, U. Wenzel1, A. White3, F. Wilde1, F. Wilms1, T. Windisch1, M. Winkler1, A. Winter1, V. Winters1, R. Wolf118, A.M. Wright29, G.A. Wurden39, P. Xanthopoulos1, S. Xu17, H. Yamada58, H. Yamaguchi41, M. Yokoyama41, M. Yoshinuma41, Q. Yu8, M. Zamanov14, M. Zanini1, M. Zarnstorff29, D. Zhang1, S. Zhou17, J. Zhu1, C. Zhu29, M. Zilker8, A. Zocco1, H. Zohm8, S. Zoletnik7 and L. Zsuga7 // 1 Max Planck Institute for Plasma Physics, Garching and Greifswald, Germany: 2 University of Greifswald, Greifswald, Germany; 3 Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, United States of America; 4 Consorzio RFX, Corso Stati Uniti, 4-35127 Padova, Italy; 5 Auburn University, Auburn, AL 36849, United States of America; 6 CIEMAT, Avenida Complutense, 40, 28040 Madrid, Spain; 7 Center for Energy Research, Konkoly-Thegeut 29-33, 1121 Budapest, Hungary; 8 Max-Planck-Institute for Plasma Physics, Boltzmannstraße 2, 85748 Garching bei München, Germany; 9 CEA Cadarache, 13115 Saint-Paul-lez-Durance, France; 10 Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany; 11 University of Cagliari, Via Universita, 40, 09124 Cagliari, Italy; 12 École Polytechnique Fédérale de Lausanne, Swiss Plasma Center, CH-1015 Lausanne, Switzerland; 13 University of Wisconsin–Madison, Engineering Drive, Madison, WI 53706, United States of America; 14 Institute of Plasma Physics, National Science Center ‘Kharkiv Institute of Physics and Technology’, Kharkiv, Ukraine; 15 The Australian National University, Acton ACT 2601, Canberra, Australia; 16 Department of Engineering, University of Palermo, Viale delle Scienze, Edificio 6, Palermo, 90128, Italy; 17 Forschungszentrum Jülich GmbH, Institut für Energie-und Klimaforschung—Plasmaphysik, 52425 Jülich, Germany; 18 Technical University of Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany; 19 Eindhoven University of Technology, 5600 MB Eindhoven, Netherlands; 20 Universitat Politècnica de Catalunya. BarcelonaTech, C. Jordi Girona, 31, 08034 Barcelona, Spain; 21 Culham Center for Fusion Energy, Abingdon OX14 3EB, United Kingdom; 22 Instituto de Plasmas e Fusao Nuclear, Av. Rovisco Pais, 1049-001 Lisboa, Portugal; 23 Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece; 24 Institute of Plasma Physics and Laser Microfusion, 23 Hery Str., 01-497 Warsaw, Poland; 25 ENEA—Centro Ricerche Frascati, Via Enrico Fermi, 45, 00044 Frascati RM, Italy; 26 Laboratory for Plasma Physics, LPP-ERM/KMS, TEC Partner, B-1000 Brussels, Belgium; 27 Dutch Institute for Fundamental Energy Research, PO Box 6336, 5600 HH Eindhoven, Netherlands; 28 University of Texas, Austin, TX, United States of America; 29 Princeton Plasma Physics Laboratory, Princeton, NJ 08543, United States of America; 30 Kyushu University, 744 Motooka Nishi-ku, Fukuoka 819-0395, Japan; 31 Aix-Marseille University, Jardin du Pharo, 58 Boulevard Charles Livon, 13007, Marseille, France; 32 Department of Physics and Astronomy, Padova University, Via Marzolo 8, 35131 Padova, Italy; 33 Department of Applied Physics, Ghent University, Sint-Pietersnieuwstraat 41 B4, 9000 Ghent, Belgium; 34 Aalto University, 02150 Espoo, Finland; 35 Department of Physics, Technical University of Denmark, Anker Engelunds Vej, 2800 Kgs Lyngby, Denmark; 36 VTT Technical Research Center of Finland Ltd., PO Box 1000, FI-02044 VTT, Finland; 37 Institute of Plasma Physics, Chinese Academy of Sciences, 230031 Hefei, Anhui, China; 38 Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN 37830, United States of America; 39 Los Alamos National Laboratory, NM 87545, United States of America; 40 Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany; 41 National Institute for Fusion Science, National Institutes of Natural Sciences, 322-6 Oroshi-cho, Toki, Gifu Prefecture 509-5292, Japan; 42 Institute for Ion Physics and Applied Physics, University of Innsbruck, Innsbruck, Austria; 43 Graz University of Technology, Rechbauerstraße 12, 8010 GRAZ, Austria; 44 Austrian Academy of Science, Doktor-Ignaz-Seipel-Platz 2, 1010 Wien, Austria; 45 Institute for Nuclear Research, prospekt Nauky 47, Kyiv 03028, Ukraine; 46 University of Opole, plac Kopernika 11a, 45-001 Opole, Poland; 47 University of Maryland, Paint Branch Drive, College Park, MA 20742, United States of America; 48 National Research Nuclear University MEPhI, 115409 Moscow, Russian Federation; 49 Department of Microelectronics and Computer Science, Lodz University of Technology, Wolczanska 221/223, 90-924 Lodz, Poland; 50 Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 7, 00185 Roma, Italy; 51 Ioffe Physical-Technical Institute of the Russian Academy of Sciences, 26 Politekhnicheskaya, St Petersburg 194021, Russian Federation; 52 Istituto di Fisica del Plasma Piero Caldirola, Via Roberto Cozzi, 53, 20125 Milano, Italy; 53 University of Szczecin, 70-453, aleja Papieza Jana Pawła II 22A, Szczecin, Poland; 54 Lawrence University, 711 E Boldt Way, Appleton, WI 54911, United States of America; 55 Physik-Department E28, Technische Universität München, 85747 Garching, Germany; 56 Universidad Carlos III de Madrid, Av. de la Universidad, 30 Madrid, Spain; 57 Yale University, New Haven, CT 06520, United States of America; 58 University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chhiab 277-0882, Japan Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.a - Per a 2030, augmentar la cooperació internacional per tal de facilitar l’accés a la investigació i a les tecnolo­gies energètiques no contaminants, incloses les fonts d’energia renovables, l’eficiència energètica i les tecnologies de combustibles fòssils avançades i menys contaminants, i promoure la inversió en infraestructures energètiques i tecnologies d’energia no contaminant

Significance: Polyp size is important for selecting the surveillance interval or treatment policy. Nevertheless, it is challenging to accurately estimate the polyp size during endoscopy. An easy and cost-effective function to assist in polyp size estimation is required. Aim: To propose a virtual scale function for endoscopy and evaluate its performance and expected accuracy. Approach: An adaptive virtual scale behavior was demonstrated. The measurement error of the virtual scale along the distance between the tip of the endoscope and the object plane was evaluated using graph paper. The accuracy of polyp size estimation by an expert endoscopist was compared with the accuracy of the biopsy forceps method using phantom images. Results: The measurement errors of the virtual scale were ≤ 0.7 mm when the distance to the graph paper, which faced the tip of the endoscope, varied from 4 to 30 mm. The accuracy with the virtual scale was significantly higher than that obtained with biopsy forceps (5.3 ± 5.5 % versus 11.9 ± 9.4 % , P

Image segmentation is a fundamental precursor to quantitative image analysis. At present, no standardised methodology exists for segmenting images of fluorescent proxies for trace evidence. Experiments evaluated (i) whether manual segmentation is reproducible within and between examiners (with three participants repeatedly tracing three images) (ii) whether manually defining a threshold level offers accurate and reproducible results (with 20 examiners segmenting 10 images), and (iii) whether a global thresholding algorithm might perform with similar accuracy, while offering improved reproducibility and efficiency (16 algorithms tested). Statistically significant differences were seen between examiners’ traced outputs. Manually thresholding produced good accuracy on average (within ±1% of the expected values), but poor reproducibility (with multiple outliers). Three algorithms (Yen, MaxEntropy, and RenyiEntropy) offered similar accuracy, with improved reproducibility and efficiency. Together, these findings suggest that appropriate algorithms could perform thresholding tasks as part of a robust workflow for reconstruction studies employing fluorescent proxies for trace evidence.

Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants. Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software. Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively. Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.

Purpose: Hippocampus contouring for radiotherapy planning is performed on MR image data due to poor anatomical visibility on computed tomography (CT) data. Deep learning methods for direct CT hippocampus auto-segmentation exist, but use MR-based training contours. We investigate if these can be replaced by CT-based contours without loss in segmentation performance. This would remove the MR not only from inference but also from training. Approach: The hippocampus was contoured by medical experts on MR and CT data of 45 patients. Convolutional neural networks (CNNs) for hippocampus segmentation on CT were trained on CT-based or propagated MR-based contours. In both cases, their predictions were evaluated against the MR-based contours considered as the ground truth. Performance was measured using several metrics, including Dice score, surface distances, and contour Dice score. Bayesian dropout was used to estimate model uncertainty. Results: CNNs trained on propagated MR contours (median Dice 0.67) significantly outperform those trained on CT contours (0.59) and also experts contouring manually on CT (0.59). Differences between the latter two are not significant. Training on MR contours results in lower model uncertainty than training on CT contours. All contouring methods (manual or CNN) on CT perform significantly worse than a CNN segmenting the hippocampus directly on MR (median Dice 0.76). Additional data augmentation by rigid transformations improves the quantitative results but the difference remains significant. Conclusions: CT-based training contours for CT hippocampus segmentation cannot replace propagated MR-based contours without significant loss in performance. However, if MR-based contours are used, the resulting segmentations outperform experts in contouring the hippocampus on CT.

Significance: Currently, tissue biopsies are sectioned into 3- to 5-μm-thick slices that are used for conventional pathology analysis. Previous work by confocal microscopy and light-sheet microscopy has shown that analyzing biopsies intact in three-dimensions (3D) is possible and may lead to a better understanding of cancer growth patterns. Although accurate, these methods require fluorescent staining of the tissue, in addition to tissue clearing. If the 3D biopsy analysis could be done sufficiently swiftly, this approach may be used for on-site assessment of the adequacy of a biopsy taken. Aim: We aim to show that, by transmission microscopy of optically cleared tissue punches, the tissue architecture can be determined without the need for fluorescent staining. Approach: Transmission microscopy is used by combining bright field microscopy with dark field and epifluorescent microscopy to compare samples that have also been analyzed by fluorescent confocal microscopy. Results: With increasing distance to the focal plane, the higher-frequency part of the spatial frequency spectrum of transmitted light is attenuated increasingly. This property is exploited for tissue segmentation, detecting whether tissue is present at a certain position in the focal plane image. Using this approach, we show that a 3D rendering of the internal cavity or tubules structure of punch biopsies, which are up to 1-mm thick, can be acquired in ≈1 min scan time per imaging modality. The images of the overall tissue architecture that are obtained are similar to those from the confocal microscopy benchmark, without requiring fluorescent staining. Conclusions: Images of the overall tissue architecture can be obtained from transmission microcopy; they are similar to those from the confocal microscopy benchmark without requiring fluorescent staining. Tissue clearing is still needed. The total scan time of the present method is significantly shorter at a fraction of the device costs.

Significance: Detection and characterization of coronary atherosclerotic plaques often need reviews of a large number of optical coherence tomography (OCT) imaging slices to make a clinical decision. However, it is a challenge to manually review all the slices and consider the interrelationship between adjacent slices. Approach: Inspired by the recent success of deep convolutional network on the classification of medical images, we proposed a ResNet-3D network for classification of coronary plaque calcification in OCT pullbacks. The ResNet-3D network was initialized with a trained ResNet-50 network and a three-dimensional convolution filter filled with zeros padding and non-zeros padding with a convolutional filter. To retrain ResNet-50, we used a dataset of ∼4860 OCT images, derived by 18 entire pullbacks from different patients. In addition, we investigated a two-phase training method to address the data imbalance. For an improved performance, we evaluated different input sizes for the ResNet-3D network, such as 3, 5, and 7 OCT slices. Furthermore, we integrated all ResNet-3D results by majority voting. Results: A comparative analysis proved the effectiveness of the proposed ResNet-3D networks against ResNet-2D network in the OCT dataset. The classification performance (F1-scores=94% for non-zeros padding and F1-score=96% for zeros padding) demonstrated the potential of convolutional neural networks (CNNs) in classifying plaque calcification. Conclusions: This work may provide a foundation for further work in extending the CNN to voxel segmentation, which may lead to a supportive diagnostic tool for assessment of coronary plaque vulnerability.

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities. ispartof: JOURNAL OF MEDICAL IMAGING vol:7 issue:4 ispartof: location:United States status: published

Significance: The rates of melanoma and nonmelanoma skin cancer are rising across the globe. Due to a shortage of board-certified dermatologists, the burden of dermal lesion screening and erythema monitoring has fallen to primary care physicians (PCPs). An adjunctive device for lesion screening and erythema monitoring would be beneficial because PCPs are not typically extensively trained in dermatological care. Aim: We aim to examine the feasibility of using a smartphone-camera-based dermascope and a USB-camera-based dermascope utilizing polarized white-light imaging (PWLI) and polarized multispectral imaging (PMSI) to map dermal chromophores and erythema. Approach: Two dermascopes integrating LED-based PWLI and PMSI with both a smartphone-based camera and a USB-connected camera were developed to capture images of dermal lesions and erythema. Image processing algorithms were implemented to provide chromophore concentrations and redness measures. Results: PWLI images were successfully converted to an alternate colorspace for erythema measures, and the spectral bandwidth of the PMSI LED illumination was sufficient for mapping of deoxyhemoglobin, oxyhemoglobin, and melanin chromophores. Both types of dermascopes were able to achieve similar relative concentration results. Conclusion: Chromophore mapping and erythema monitoring are feasible with PWLI and PMSI using LED illumination and smartphone-based cameras. These systems can provide a simpler, more portable geometry and reduce device costs compared with interference-filter-based or spectrometer-based clinical-grade systems. Future research should include a rigorous clinical trial to collect longitudinal data and a large enough dataset to train and implement a machine learning-based image classifier.

Purpose:\ud \ud The pattern of dense tissue on a mammogram appears to provide additional information than overall density for risk assessment, but there has been little consistency in measures of texture identified. The purpose of this study is thus to validate a mammographic texture feature developed from a previous study in a new setting.\ud \ud Approach:\ud \ud A case–control study (316 invasive cases and 1339 controls) of women in Virginia, USA was used to validate a mammographic texture feature (MMTEXT) derived in a independent previous study. Analysis of predictive ability was adjusted for age, demographic factors, questionnaire risk factors (combined through the Tyrer-Cuzick model), and optionally BI-RADS breast density. Odds ratios per interquartile range (IQ-OR) in controls were estimated. Subgroup analysis assessed heterogeneity by mode of cancer detection (94 not detected by mammography).\ud \ud Results:\ud \ud MMTEXT was not a significant risk factor at 0.05 level after adjusting for classical risk factors (IQ-OR = 1.16, 95%CI 0.92 to 1.46), nor after further adjustment for BI-RADS density (IQ-OR = 0.92, 95%CI 0.76 to 1.10). There was weak evidence that MMTEXT was more predictive for cancers that were not detected by mammography (unadjusted for density: IQ-OR = 1.46, 95%CI 0.99 to 2.15 versus 1.03, 95%CI 0.79 to 1.35, Phet 0.10; adjusted for density: IQ-OR = 1.11, 95%CI 0.70 to 1.77 versus 0.76, 95%CI 0.55 to 1.05, Phet 0.21).\ud \ud Conclusions:\ud \ud MMTEXT is unlikely to be a useful imaging marker for invasive breast cancer risk assessment in women attending mammography screening. Future studies may benefit from a larger sample size to confirm this as well as developing and validating other measures of risk. This negative finding demonstrates the importance of external validation.

New increasingly complex in vitro cancer cell models are being developed. These new models seem to represent the cell behavior in vivo more accurately and have better physiological relevance than prior models. An efficient testing method for selecting the most optimal drug treatment does not exist to date. One proposed solution to the problem involves isolation of cancer cells from the patients' cancer tissue, after which they are exposed to potential drugs alone or in combinations to find the most optimal medication. To achieve this goal, methods that can efficiently quantify and analyze changes in tested cell are needed. Our study aimed to detect and segment cells and structures from cancer cell cultures grown on vascular structures in phase-contrast microscope images using U-Net neural networks to enable future drug efficacy assessments. We cultivated prostate carcinoma cell lines PC3 and LNCaP on the top of a matrix containing vascular structures. The cells were imaged with a Cell-IQ phase-contrast microscope. Automatic analysis of microscope images could assess the efficacy of tested drugs. The dataset included 36 RGB images and ground-truth segmentations with mutually not exclusive classes. The used method could distinguish vascular structures, cells, spheroids, and cell matter around spheroids in the test images. Some invasive spikes were also detected, but the method could not distinguish the invasive cells in the test images. publishedVersion

Significance: Spatial frequency domain imaging (SFDI) is a diffuse optical measurement technique that can quantify tissue optical absorption (μa) and reduced scattering (μ0 s) on a pixelby-pixel basis. Measurements of μa at different wavelengths enable the extraction of molar concentrations of tissue chromophores over a wide field, providing a noncontact and label-free means to assess tissue viability, oxygenation, microarchitecture, and molecular content. We present here openSFDI: an open-source guide for building a low-cost, small-footprint, threewavelength SFDI system capable of quantifying μa and μ0 s as well as oxyhemoglobin and deoxyhemoglobin concentrations in biological tissue. The companion website provides a complete parts list along with detailed instructions for assembling the openSFDI system. Aim: We describe the design of openSFDI and report on the accuracy and precision of optical property extractions for three different systems fabricated according to the instructions on the openSFDI website. Approach: Accuracy was assessed by measuring nine tissue-simulating optical phantoms with a physiologically relevant range of μa and μ0 s with the openSFDI systems and a commercial SFDI device. Precision was assessed by repeatedly measuring the same phantom over 1 h. Results: The openSFDI systems had an error of 0 6% in μa and −2 3% in μ0 s, compared to a commercial SFDI system. Bland–Altman analysis revealed the limits of agreement between the two systems to be 0.004 mm−1 for μa and −0.06 to 0.1 mm−1 for μ0 s. The openSFDI system had low drift with an average standard deviation of 0.0007 mm−1 and 0.05 mm−1 in μa and μ0 s, respectively. Conclusion: The openSFDI provides a customizable hardware platform for research groups seeking to utilize SFDI for quantitative diffuse optical imaging. Funding agencies: U.S. Department of Defense (DoD)United States Department of Defense [W81XWH-15-1-0070]; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation; European Research Council under the European UnionEuropean Research Council (ERC) [715737]; Fre

Noninvasive in vivo imaging of microcirculatory blood flow or perfusion (average speed times concentration of moving red blood cells) is of interest in several clinical applications, including monitoring of burn wounds1,2 and investigation of peripheral arterial disease,3 as well as several others.4 One technique that has gained a lot of focus in the last decades is laser speckle contrast imaging [LSCI; sometimes laser speckle contrast analysis (LASCA)] in which tissue is illuminated with a laser and the resulting speckle pattern is detected with a camera. Light that scatters when interacting with moving particles in the tissue will obtain a Doppler frequency shift depending on the velocity of the particles. The interference of light with different frequencies will give rise to fluctuations in the speckle pattern formed on the imaging sensor. The movement of the speckles on the sensor will cause an image blur that increases with exposure time. The local spatial statistics of this blur can be related to the movement of particles in the tissue, as is done in LSCI.5 LSCI has been increasingly used over laser Doppler imaging mainly due to its simple setup and fast acquisition and data processing. However, conventional LSCI has drawbacks such as a nonlinear response to perfusion, dependency to static scattering contrast, and a high variability in the presence of noise.5,6 Recent work has gone into calibrating and correcting for these issues to make LSCI a more accurate technique.7 Despite this, while it is generally accepted that LSCI is related to microcirculatory perfusion, the relationship is complex and a direct mapping is still not known.4,8 For the older laser Doppler flowmetry (LDF) technique, the relation to microcircular perfusion is better understood, where it is, for example, possible to theoretically show that the perfusion estimate is linearly related to the flow speed.9,10 To address the nonlinearity of LSCI to perfusion, Parthasarathy et al.11 proposed a new setup using multiple exposure times. The technique, called multi-exposure laser speckle contrast imaging (MELSCI, sometimes MESI), obtains information about the speckle motion blur at various exposures, enabling more advanced models to be used when estimating perfusion from the contrast.5,12 Several approaches to capture multi-exposure images have been proposed. The initial approach by Parthasarathy et al.11 was to use a time-modulated laser to achieve multi-exposure images. By capturing all images with a fixed exposure time but only illuminating the tissue during parts of that time, multi-exposures can be generated. However, a problem with this approach is that images with different exposure times are separated in time since they are captured sequentially. Dragojevic et al.13 suggested another approach that addressed this drawback. This was based on a high-speed camera, only capturing images at the shortest exposure time required, and in postprocessing adding successive images together to create longer exposure times. These synthetic exposure times are valid if the interframe delay in the camera is negligible. This method is faster and more accurate than previous methods due to all exposure times essentially being captured simultaneously, but it produces immense amounts of data that must be transferred and processed. At the time, this was, therefore, limited to an offline technique. Hence, MELSCI has so far been held back by technical limitations in both the imaging setup and the computation time of the models. To address this problem of a data and processing bottleneck, we have previously presented a system for synthetic MELSCI,14 based on a high-speed 1000-frames per second (fps) 1-megapixel camera directly interfaced to a field programmable gate array (FPGA) performing the multi-exposure contrast algorithm outlined by Dragojevic13 in real-time. Processed contrast images were sent to a computer, massively reducing the data throughput requirements. This system could produce multi-exposure contrast images at 15.6 fps, using all available frames from the 1000-fps camera, without any loss of data. However, due to insufficient processing power, the system was not able to transfer the processed images to the computer fast enough, and thus could not provide a real-time video-rate output. Furthermore, while the calculation of multi-exposure contrast images was fast enough, at the time there was no multi-exposure perfusion algorithm fast enough to keep up with the continuous stream of contrast images. The model proposed by Parthasarathy et al.11 and later refined by Kazmi et al.15 requires nonlinear fitting to extract perfusion-related parameters from the multi-exposure contrast. This process must be performed individually in each pixel for full field perfusion images, which is not feasible in real-time with satisfactory frame rates.6 We have previously presented a method using machine learning and simulated data from thousands of Monte Carlo tissue models from which we train an artificial neural network (ANN) to translate multi-exposure contrast to laser Doppler perfusion. We showed that this technique not only achieves a model-inference time of

X-ray cone-beam holotomography of unstained tissue from the human central nervous system reveals details down to subcellular length scales. This visualization of variations in the electron density of the sample is based on phase-contrast techniques using intensities formed by self-interference of the beam between object and detector. Phase retrieval inverts diffraction and overcomes the phase problem by constraints such as several measurements at different Fresnel numbers for a single projection. Therefore, the object-to-detector distance (defocus) can be varied. However, for cone-beam geometry, changing defocus changes magnification, which can be problematic in view of image processing and resolution. Alternatively, the photon energy can be altered (multi-E). Far from absorption edges, multi-E data yield the wavelength-independent electron density. We present the multi-E holotomography at the Göttingen Instrument for Nano-Imaging with X-Rays (GINIX) setup of the P10 beamline at Deutsches Elektronen-Synchrotron. The instrument is based on a combined optics of elliptical mirrors and an x-ray waveguide positioned in the focal plane for further coherence, spatial filtering, and high numerical aperture. Previous results showed the suitability of this instrument for nanoscale tomography of unstained brain tissue. We demonstrate that upon energy variation, the focal spot is stable enough for imaging. To this end, a double-crystal monochromator and automated alignment routines are required. Three tomograms of human brain tissue were recorded and jointly analyzed using phase retrieval based on the contrast transfer function formalism generalized to multiple photon energies. Variations of the electron density of the sample are successfully reconstructed.

Non-contact vital sign monitoring enables the estimation of vital signs, such as heart rate, respiratory rate and oxygen saturation (SpO2), by measuring subtle color changes on the skin surface using a video camera. For patients in a hospital ward, the main challenges in the development of continuous and robust non-contact monitoring techniques are the identification of time periods and the segmentation of skin regions of interest (ROIs) from which vital signs can be estimated. We propose a deep learning framework to tackle these challenges. Approach: This paper presents two convolutional neural network (CNN) models. The first network was designed for detecting the presence of a patient and segmenting the patient’s skin area. The second network combined the output from the first network with optical flow for identifying time periods of clinical intervention so that these periods can be excluded from the estimation of vital signs. Both networks were trained using video recordings from a clinical study involving 15 pre-term infants conducted in the high dependency area of the neonatal intensive care unit (NICU) of the John Radcliffe Hospital in Oxford, UK. Main results: Our proposed methods achieved an accuracy of 98.8% for patient detection, a mean intersection-over-union (IOU) score of 88.6% for skin segmentation and an accuracy of 94.5% for clinical intervention detection using two-fold cross validation. Our deep learning models produced accurate results and were robust to different skin tones, changes in light conditions, pose variations and different clinical interventions by medical staff and family visitors. Significance: Our approach allows cardio-respiratory signals to be continuously derived from the patient’s skin during which the patient is present and no clinical intervention is undertaken.

We present a multispectral fundus camera that performs fast imaging of the ocular posterior pole in the visible and near-infrared (400 to 1300 nm) wavelengths through 15 spectral bands, using a flashlight source made of light-emitting diodes, and CMOS and InGaAs cameras. We investigate the potential of this system for visualizing occult and overlapping structures of the retina in the unexplored wavelength range beyond 900 nm, in which radiation can penetrate deeper into the tissue. Reflectance values at each pixel are also retrieved from the acquired images in the analyzed spectral range. The available spectroscopic information and the visualization of retinal structures, specifically the choroidal vasculature and drusen-induced retinal pigment epithelium degeneration, which are hardly visible in conventional color fundus images, underline the clinical potential of this system as a new tool for ophthalmic diagnosis.

X-ray luminescence computed tomography (XLCT) is an emerging hybrid imaging modality which has the potential for achieving both high sensitivity and spatial resolution simultaneously. For the narrow x-ray beam-based XLCT imaging, based on previous work, a spatial resolution of about double the x-ray beam size can be achieved using a translate/rotate scanning scheme, taking step sizes equal to the x-ray beam width. To break the current spatial resolution limit, we propose a scanning strategy achieved by reducing the scanning step size to be smaller than the x-ray beam size. We performed four sets of numerical simulations and a phantom experiment using cylindrical phantoms and have demonstrated that our proposed scanning method can greatly improve the XLCT-reconstructed image quality compared with the traditional scanning approach. In our simulations, by using a fixed x-ray beam size of 0.8 mm, we were able to successfully reconstruct six embedded targets as small as 0.5 mm in diameter and with the same edge-to-edge distances by using a scanning step as small as 0.2 mm which is a 1.6 times improvement in the spatial resolution compared with the traditional approach. Lastly, the phantom experiment further demonstrated the efficacy of our proposed method in improving the XLCT image quality, with all image quality metrics improving as the step size decreased.

Several noninvasive imaging techniques have been developed to monitor the health of skin and enhance the diagnosis of skin diseases. Among them, skin elastography is a popular technique used to measure the elasticity of the skin. A change in the elasticity of the skin can influence its natural frequencies and mode shapes. We propose a technique to use the resonant frequencies and mode shapes of the skin to monitor its health. Our study demonstrates how the resonant frequencies and mode shapes of skin can be obtained using numerical and experimental analysis. In our study, natural frequencies and mode shapes are obtained via two methods: (1) finite element analysis: an eigensolution is performed on a finite element model of normal skin, including stratum corneum, epidermis, dermis, and subcutaneous layers and (2) digital image correlation (DIC): several in-vivo measurements have been performed using DIC. The experimental results show a correlation between the DIC and FE results suggesting a noninvasive method to obtain vibration properties of the skin. This method can be further examined to be eventually used as a method to differentiate healthy skin from diseased skin. Prevention, early diagnosis, and treatment are critical in helping to reduce the incidence, morbidity, and mortality associated with skin cancer; thus, making the current study significant and important in the field of skin biomechanics.

We introduce a method for quantitative hyperspectral optical imaging in the spatial frequency domain (hs-SFDI) to image tissue absorption (μa) and reduced scattering (μs') parameters over a broad spectral range. The hs-SFDI utilizes principles of spatial scanning of the spectrally dispersed output of a supercontinuum laser that is sinusoidally projected onto the tissue using a digital micromirror device. A scientific complementary metal-oxide-semiconductor camera is used for capturing images that are demodulated and analyzed using SFDI computational models. The hs-SFDI performance is validated using tissue-simulating phantoms over a range of μa and μs' values. Quantitative hs-SFDI images are obtained from an ex-vivo beef sample to spatially resolve concentrations of oxy-, deoxy-, and met-hemoglobin, as well as water and fat fractions. Our results demonstrate that the hs-SFDI can quantitatively image tissue optical properties with 1000 spectral bins in the 580- to 950-nm range over a wide, scalable field of view. With an average accuracy of 6.7% and 12.3% in μa and μs', respectively, compared to conventional methods, hs-SFDI offers a promising approach for quantitative hyperspectral tissue optical imaging. Funding agencies: National Institute of Health (NIH) NIBIB Biomedical Technology Research Center LAMMP [P41EB015890]; NIH [R21EB020953]; Military Medical Photonics Program: AFOSR [FA9550-17-1-0193]; Arnold and Mabel Beckman Foundation

Significance: Parallel phase-shifting digital holographic microscope (PPSDHM) is powerful for three-dimensional (3D) measurements of dynamic specimens. However, the PPSDHM reported previously was directly fixed on the optical bench and imposed difficulties case, thus it is required to modify the specification of the microscope or transport the microscope to another location. Aim: We present a modularized PPSDHM. We construct the proposed PPSDHM and demonstrate the 3D measurement capability of the PPSDHM. Approach: The PPSDHM was designed as an inverted microscope to record transparent objects and modularized by integrating the optical elements of the PPSDHM on an optical breadboard. To demonstrate the effectiveness of the PPSDHM, we recorded a 3D motion-picture of moving Volvoxes at 1000 frames/s and carried out 3D tracking of the Volvoxes. Results: The PPSDHM was practically realized and 3D images of objects were successfully reconstructed from holograms recorded with a single-shot exposure. The 3D trajectories of Volvoxes were obtained from the reconstructed images. Conclusions: We established a modularized PPSDHM that is capable of 3D image acquisition by integrating the optical elements of the PPSDHM on an optical breadboard. The recording capability of 3D motion-pictures of dynamic specimens was experimentally demonstrated by the PPSDHM.

Automatic characterisation and detection of dirt particles in pulp and paper plays a pivotal role in the papermaking industry. Machine vision provides many potential advantages in terms of speed, accuracy and repeatability. Such systems make use of image processing algorithms which aim at separating paper and pulp impurities from the background. The most common approach is based on image thresholding, which consists of determining a set of intensity values that split an image into one or more classes, each representing either the background (i.e., an area with no defects) or an area with some types of contraries. In this paper, we present a quantitative experimental evaluation of four image thresholding methods (i.e., Otsu’s, Kapur’s, Kittler’s and Yen’s) for dirt analysis in paper. The results show that Kittler’s method is the most stable and reliable for this task.

We present a sequential, two-step procedure based on machine vision for detecting and characterizing impurities in paper. The method is based on a preliminary classification step to differentiate defective paper patches (i.e., with impurities) from non-defective ones (i.e., with no impurities), followed by a thresholding step to separate the impurities from the background. This approach permits to avoid the artifacts which occur when thresholding is applied to paper samples that contain no impurities. We discuss and compare different solutions and methods to implement the procedure and experimentally validate it on a datasets of 11 paper classes. The results show that a marked increase in detection accuracy can be obtained with the two-step procedure in comparison with thresholding alone.

This thesis treats static and dynamic surface characterization using radiometric and Fourier optical methods. A Fourier optical method has been developed for real time image processing in paper production and printing applications. It has been shown that the method can be used to measure crepe frequency, an important parameter in tissue paper production, as well as to monitor the wire mark pattern at paper web velocities of up to 20 m/s. The wire mark pattern has been used to measure dimensional variations across a paper web. These are important for the mechanical properties of paper. Imaging of the moving surfaces onto a spatial light modulator, necessary for Fourier optical analysis of opaque objects, constitutes a motion blur problem. This problem has been solved by means of optical motion compensation using a rotating mirror. A rotating mirror system has also been developed for the inspection of small particles fixed to a rotating sample disc. The optical motion compensation configurations have made exposure times of more than two orders of magnitude longer than the exposure time without compensation possible. A light scattering model for opaque objects, for example coated paper, has also been developed and verified, with a coefficient of determination between theory and measurement ranging from r2=0.84 to r2=0.98, on various paper samples. The light scattering model has been used in the development of an instrument based on the photometric stereo principle. In this instrument the reflectance (or color) and topography of opaque samples are determined from two or more images of the sample illuminated from different directions. The method has been successfully used for studies of the relation between topography and print results in gravure and flexographic printing. Comparisons of surface height profiles measured with the photometric stereo method and profiles obtained with mechanical and optical scanning stylus instruments have shown coefficients of determination of up to r2=0.97. The main advantages of the method are the high speed, the scalability and the ability to obtain reflectance and surface height maps of a surface simultaneously.