20.8.3 Tomographic Object Construction, Object Extraction, Analysis, Organs

Chapter Contents (Back)
Reconstruction. Tomography. Segmentation.
See also Abdominal Seqmentation, Multi-Organ Segmentation.
See also Backprojection in Tomographic Image Reconstruction.

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CGIP(5), No. 4, December 1976, pp. 470-483.
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The importance of ray pathlengths when measuring objects in maximum intensity projection images,
MedImg(15), No. 4, August 1996, pp. 568-579.
IEEE Top Reference. 0203
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Kamath, M., Chaudhuri, S., Desai, U.B.,
Direct Parametric Object Detection in Tomographic-Images,
IVC(16), No. 9-10, July 1998, pp. 669-676.
Elsevier DOI 9808
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Brodzik, A.K.[Andrzej K.], Mooney, J.M.[Jonathan M.],
Convex Projections Algorithm for Restoration of Limited-Angle Chromotomographic Images,
JOSA-A(16), No. 2, February 1999, pp. 246-257. BibRef 9902

Feng, H.H.[Hai-Hua], Karl, W.C.[William Clem], Castanon, D.A.[David A.],
A curve evolution approach to object-based tomographic reconstruction,
IP(12), No. 1, January 2003, pp. 44-57.
IEEE DOI 0301
BibRef
Earlier: A1, A3, A2:
A Curve Evolution Approach for Image Segmentation Using Adaptive Flows,
ICCV01(II: 494-499).
IEEE DOI 0106
BibRef
And:
Object-based Reconstruction Using Coupled Tomographic Flows,
ICIP00(Vol II: 625-628).
IEEE DOI 0008
BibRef
And:
Tomographic Reconstruction using Curve Evolution,
CVPR00(I: 361-366).
IEEE DOI 0005
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Shi, Y.G.[Yong-Gang], Karl, W.C., Castafion, D.A.,
Dynamic tomography using curve evolution with spatial-temporal regularization,
ICIP02(II: 629-632).
IEEE DOI 0210
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Brankov, J.G., Yang, Y.Y.[Yong-Yi], Wernick, M.N.,
Tomographic Image Reconstruction Based on a Content-Adaptive Mesh Model,
MedImg(23), No. 2, February 2004, pp. 202-212.
IEEE Abstract. 0403
BibRef
Earlier:
Content-adaptive 3D mesh modeling for representation of volumetric images,
ICIP02(III: 849-852).
IEEE DOI 0210
BibRef
Earlier:
Tomographic Image Reconstruction Using Content-Adaptive Mesh Modeling,
ICIP01(I: 690-693).
IEEE DOI 0108

See also Fast Approach for Accurate Content-Adaptive Mesh Generation, A. BibRef

Brankov, J.G., Yang, Y.Y.[Yong-Yi], Galatsanos, N.P.,
Image restoration using content-adaptive mesh modeling,
ICIP03(II: 997-1000).
IEEE DOI 0312
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Yang, Y.Y.[Yong-Yi], Brankov, J.G., Wernick, M.N.,
Content-adaptive mesh modeling for fully-3D tomographic image reconstruction,
ICIP02(II: 621-624).
IEEE DOI 0210
BibRef

Brankov, J.G.[Jovan G.], Djordjevic, J.[Jaksa], Wernick, M.N.[Miles N.], Galatsanos, N.P.[Nikolas P.],
Tomographic Image Reconstruction for Systems with Partially-Known Blur,
ICIP99(III:881-885).
IEEE DOI BibRef 9900

Mesarovic, V.Z., Galatsanos, N.P., Wernick, M.N.,
Iterative maximum a posteriori (MAP) restoration from partially-known blur for tomographic reconstruction,
ICIP95(II: 512-515).
IEEE DOI 9510
BibRef

Soussen, C., Mohammad-Djafari, A.,
Polygonal and Polyhedral Contour Reconstruction in Computed Tomography,
IP(13), No. 11, November 2004, pp. 1507-1523.
IEEE DOI 0411
BibRef
Earlier:
Closed Surface Reconstruction in X-ray Tomography,
ICIP01(I: 718-721).
IEEE DOI 0108
BibRef

Mouravliansky, N., Matsopoulos, G.K., Delibasis, K., Asvestas, P., Nikita, K.S.,
Combining a morphological interpolation approach with a surface reconstruction method for the 3-D representation of tomographic data,
JVCIR(15), No. 4, December 2004, pp. 565-579.
Elsevier DOI 0711
Medical interpolation; Mathematical morphology; 3-D visualization; Surface reconstruction; Marching Cubes Algorithm BibRef

Sitek, A., Huesman, R.H., Gullberg, G.T.,
Tomographic reconstruction using an adaptive tetrahedral mesh defined by a point cloud,
MedImg(25), No. 9, September 2006, pp. 1172-1179.
IEEE DOI 0609
BibRef

Murphy, R.J., Yan, S., O'Sullivan, J.A., Snyder, D.L., Whiting, B.R., Politte, D.G., Lasio, G., Williamson, J.F.,
Pose Estimation of Known Objects During Transmission Tomographic Image Reconstruction,
MedImg(25), No. 10, October 2006, pp. 1392-1404.
IEEE DOI 0609
BibRef

Balazs, P.[Peter],
A decomposition technique for reconstructing discrete sets from four projections,
IVC(25), No. 10, 1 October 2007, pp. 1609-1619.
Elsevier DOI 0709
Discrete tomography; Reconstruction algorithm; Decomposable discrete set; Q-convexity; hv-Convexity BibRef

Varga, L.G.[László G.], Nyúl, L.G.[László G.], Nagy, A.[Antal], Balázs, P.[Péter],
Local and global uncertainty in binary tomographic reconstruction,
CVIU(129), No. 1, 2014, pp. 52-62.
Elsevier DOI 1411
Binary tomography BibRef

Varga, L.G.[László G.], Lékó, G.[Gábor], Balázs, P.[Péter],
Grayscale Uncertainty of Projection Geometries and Projections Sets,
IWCIA20(123-138).
Springer DOI 2009
BibRef

Lékó, G.[Gábor], Balázs, P.[Péter], Varga, L.G.[László G.],
Projection Selection for Binary Tomographic Reconstruction Using Global Uncertainty,
ICIAR18(3-10).
Springer DOI 1807
BibRef

Lékó, G.[Gábor], Domány, S.[Szilveszter], Balázs, P.[Péter],
Uncertainty Based Adaptive Projection Selection Strategy for Binary Tomographic Reconstruction,
CAIP19(II:74-84).
Springer DOI 1909
BibRef

Balázs, P.[Péter], Batenburg, K.J.[Kees Joost],
A Central Reconstruction Based Strategy for Selecting Projection Angles in Binary Tomography,
ICIAR12(I: 382-391).
Springer DOI 1206
BibRef

Balázs, P.[Péter],
Reconstruction of Canonical hv-Convex Discrete Sets from Horizontal and Vertical Projections,
IWCIA09(280-288).
Springer DOI 0911
BibRef
Earlier:
Reconstruction of Binary Images with Few Disjoint Components from Two Projections,
ISVC08(II: 1147-1156).
Springer DOI 0812
BibRef
And:
On the Number of hv -Convex Discrete Sets,
IWCIA08(xx-yy).
Springer DOI 0804
BibRef
Earlier:
Generation and Empirical Investigation of hv -Convex Discrete Sets,
SCIA07(344-353).
Springer DOI 0706
CT Reconstructions. BibRef

Balázs, P.[Péter], Gara, M.[Mihály],
An Evolutionary Approach for Object-Based Image Reconstruction Using Learnt Priors,
SCIA09(520-529).
Springer DOI 0906
BibRef
Earlier:
Decision Trees in Binary Tomography for Supporting the Reconstruction of hv-Convex Connected Images,
ACIVS08(xx-yy).
Springer DOI 0810
BibRef

Saucan, E.[Emil], Appleboim, E.[Eli], Barak-Shimron, E.[Efrat], Lev, R.[Ronen], Zeevi, Y.Y.[Yehoshua Y.],
Local versus Global in Quasi-Conformal Mapping for Medical Imaging,
JMIV(32), No. 3, November 2008, pp. xx-yy.
Springer DOI 0810
BibRef

Saucan, E.[Emil], Appleboim, E.[Eli], Zeevi, Y.Y.[Yehoshua Y.],
Sampling and Reconstruction of Surfaces and Higher Dimensional Manifolds,
JMIV(30), No. 1, January 2008, pp. 105-123.
Springer DOI 0801
BibRef
And: JMIV(34), No. 3, July 2009, pp. xx-yy.
Springer DOI 0906
BibRef
Earlier:
Geometric Sampling of Manifolds for Image Representation and Processing,
SSVM07(907-918).
Springer DOI 0705
Sampling theorem for embedding images in manifolds. BibRef

Saucan, E., Wolansky, G., Appleboim, E., Zeevi, Y.Y.,
Combinatorial Ricci Curvature and Laplacians for Image Processing,
CISP09(1-6).
IEEE DOI 0910
BibRef

Appleboim, E.[Eli], Saucan, E.[Emil], Zeevi, Y.Y.[Yehoshua Y.],
Quasi-conformal Flat Representation of Triangulated Surfaces for Computerized Tomography,
CVAMIA06(155-165).
Springer DOI 0605
BibRef

Appleboim, E.[Eli], Saucan, E.[Emil], Zeevi, Y.Y.[Yehoshua Y.], Zeitoun, O.[Ofir],
Quasi-isometric and Quasi-conformal Development of Triangulated Surfaces for Computerized Tomography,
IWCIA06(361-374).
Springer DOI 0606
BibRef

Saucan, E.[Emil],
Isometric Embeddings in Imaging and Vision: Facts and Fiction,
JMIV(43), No. 2, June 2012, pp. 143-155.
WWW Link. 1204
BibRef

Wang, H.K.[Hong-Kai], Stout, D.B., Chatziioannou, A.F.,
Estimation of Mouse Organ Locations Through Registration of a Statistical Mouse Atlas With Micro-CT Images,
MedImg(31), No. 1, January 2012, pp. 88-102.
IEEE DOI 1201
BibRef

Bagci, U., Chen, X., Udupa, J.K.,
Hierarchical Scale-Based Multiobject Recognition of 3-D Anatomical Structures,
MedImg(31), No. 3, March 2012, pp. 777-789.
IEEE DOI 1203
BibRef

Dufour, P.A., Ceklic, L., Abdillahi, H., Schroder, S., de Dzanet, S., Wolf-Schnurrbusch, U., Kowal, J.,
Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints,
MedImg(32), No. 3, March 2013, pp. 531-543.
IEEE DOI 1303
OCT: Optical Coherence Tomography. BibRef

Xu, X.[Xu], Cui, Y.[Yi], Guo, S.[Shuxu],
Statistical Edge Detection in CT Image by Kernel Density Estimation and Mean Square Error Distance,
IEICE(E96-D), No. 5, May 2013, pp. 1162-1170.
WWW Link. 1305
BibRef

Mendonca, P.R.S., Lamb, P., Sahani, D.V.,
A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images,
MedImg(33), No. 1, January 2014, pp. 99-116.
IEEE DOI 1402
computerised tomography BibRef

Bajger, M.[Mariusz], Lee, G.[Gobert], Caon, M.[Martin],
3D Segmentation for Multi-Organs in CT Images,
ELCVIA(12), No. 2, 2013, pp. xx-yy.
DOI Link 1403
BibRef

Liu, H.[Hao], Zhu, G.H.[Guan-Hua], Zhao, J.N.[Jian-Ning], Qian, H.B.[Hong-Bo], Dai, N.[Ning],
Recognition of Occlusions in CT Images Using a Curve-Based Parameterization Method,
IJIG(13), No. 04, 2013, pp. 1350018.
DOI Link 1404
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Sampedro, F.[Frederic], Escalera, S.[Sergio], Puig, A.[Anna],
Iterative multi-class multi-scale stacked sequential learning: Definition and application to medical volume segmentation,
PRL(46), No. 1, 2014, pp. 1-10.
Elsevier DOI 1407
Machine learning BibRef

Sampedro, F.[Frederic], Escalera, S.[Sergio],
Spatial codification of label predictions in multi-scale stacked sequential learning: a case study on multi-class medical volume segmentation,
IET-CV(9), No. 3, 2015, pp. 439-446.
DOI Link 1507
image classification BibRef

Kainz, B., Steinberger, M., Wein, W., Kuklisova-Murgasova, M., Malamateniou, C., Keraudren, K., Torsney-Weir, T., Rutherford, M., Aljabar, P., Hajnal, J.V., Rueckert, D.,
Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices,
MedImg(34), No. 9, September 2015, pp. 1901-1913.
IEEE DOI 1509
Approximation methods BibRef

Martin, L., Tuysuzoglu, A., Karl, W.C., Ishwar, P.,
Learning-Based Object Identification and Segmentation Using Dual-Energy CT Images for Security,
IP(24), No. 11, November 2015, pp. 4069-4081.
IEEE DOI 1509
computerised tomography BibRef

Barquero, H., Brasse, D.,
Small Animal In Vivo X-Ray Tomosynthesis: Anatomical Relevance of the Reconstructed Images,
MedImg(35), No. 2, February 2016, pp. 373-380.
IEEE DOI 1602
Animals BibRef

Popuri, K., Cobzas, D., Esfandiari, N., Baracos, V., Jagersand, M.,
Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle,
MedImg(35), No. 2, February 2016, pp. 512-520.
IEEE DOI 1602
Computed tomography BibRef

Jimenez-del-Toro, O., Müller, H., Krenn, M., Gruenberg, K., Taha, A.A., Winterstein, M., Eggel, I., Foncubierta-Rodríguez, A., Goksel, O., Jakab, A., Kontokotsios, G., Langs, G., Menze, B.H., Salas Fernandez, T., Schaer, R., Walleyo, A., Weber, M.A., Dicente Cid, Y., Gass, T., Heinrich, M., Jia, F., Kahl, F., Kechichian, R., Mai, D., Spanier, A.B., Vincent, G., Wang, C., Wyeth, D., Hanbury, A.,
Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks,
MedImg(35), No. 11, November 2016, pp. 2459-2475.
IEEE DOI 1609
Anatomical structure BibRef

Filho, P.P.R.[Pedro P. Rebouças], de Souza Rebouças, E.[Elizângela], Marinho, L.B.[Leandro B.], Sarmento, R.M.[Róger M.], Tavares, J.M.R.S.[Joăo Manuel R.S.], de Albuquerque, V.H.C.[Victor Hugo C.],
Analysis of human tissue densities: A new approach to extract features from medical images,
PRL(94), No. 1, 2017, pp. 211-218.
Elsevier DOI 1708
Medical, imaging BibRef

Bieth, M., Peter, L., Nekolla, S.G., Eiber, M., Langs, G., Schwaiger, M., Menze, B.H.,
Segmentation of Skeleton and Organs in Whole-Body CT Images via Iterative Trilateration,
MedImg(36), No. 11, November 2017, pp. 2276-2286.
IEEE DOI 1711
Biomedical imaging, Bones, Computed tomography, Context, Image segmentation, Radio frequency, Medical Imaging, Segmentation BibRef

Mechlem, K., Ehn, S., Sellerer, T., Braig, E., Münzel, D., Pfeiffer, F., Noël, P.B.,
Joint Statistical Iterative Material Image Reconstruction for Spectral Computed Tomography Using a Semi-Empirical Forward Model,
MedImg(37), No. 1, January 2018, pp. 68-80.
IEEE DOI 1801
calibration, computerised tomography, image reconstruction, iterative methods, medical image processing, optimisation, statistical iterative reconstruction BibRef

Xu, J., Noo, F., Tsui, B.M.W.,
A Direct Algorithm for Optimization Problems With the Huber Penalty,
MedImg(37), No. 1, January 2018, pp. 162-172.
IEEE DOI 1801
biological tissues, computerised tomography, dynamic programming, image denoising, image reconstruction, image restoration, total variation BibRef

Manivannan, S., Li, W., Zhang, J., Trucco, E., McKenna, S.J.,
Structure Prediction for Gland Segmentation With Hand-Crafted and Deep Convolutional Features,
MedImg(37), No. 1, January 2018, pp. 210-221.
IEEE DOI 1801
image classification, image segmentation, medical image processing, pattern clustering, segmentation BibRef

McKenna, S.J.[Stephen J.], Amaral, T.[Telmo], Plötz, T.[Thomas], Kyriazakis, I.[Ilias],
Multi-part segmentation for porcine offal inspection with auto-context and adaptive atlases,
PRL(112), 2018, pp. 290-296.
Elsevier DOI 1809
BibRef
Earlier: A2, A4, A1, A3:
Weighted atlas auto-context with application to multiple organ segmentation,
WACV16(1-9)
IEEE DOI 1606
Multi-class segmentation, Auto-context, Atlas-based segmentation, Automated inspection. Computational modeling BibRef

Ngom, N.F.[Ndeye Fatou], Ndiaye, C.H.T.C.[Cheikh H. T. C.], Niang, O.[Oumar], Sidibe, S.[Samba],
Shape Descriptors for Porous Media Analysis Using Computed Tomography Images,
IJIG(18), No. 02, 2018, pp. 1850011.
DOI Link 1804
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Novikov, A.A., Major, D., Wimmer, M., Lenis, D., Bühler, K.,
Deep Sequential Segmentation of Organs in Volumetric Medical Scans,
MedImg(38), No. 5, May 2019, pp. 1207-1215.
IEEE DOI 1905
Image segmentation, Computer architecture, Shape, Training, convolutional LSTM BibRef

Xu, X., Zhou, F., Liu, B., Fu, D., Bai, X.,
Efficient Multiple Organ Localization in CT Image Using 3D Region Proposal Network,
MedImg(38), No. 8, August 2019, pp. 1885-1898.
IEEE DOI 1908
Computed tomography, Biological systems, Object detection, region proposal network BibRef

Wu, X.D.[Xiao-Dan], Li, H.B.[Hai-Bo], Xu, X.H.[Xiao-Hui], Wei, H.F.[Hua-Feng],
CT lesion recognition algorithm based on improved particle reseeding method,
PRL(125), 2019, pp. 119-123.
Elsevier DOI 1909
CT lesion, Improved particle reseeding method, topography BibRef

Zhou, S., Nie, D., Adeli, E., Yin, J., Lian, J., Shen, D.,
High-Resolution Encoder-Decoder Networks for Low-Contrast Medical Image Segmentation,
IP(29), No. 1, 2020, pp. 461-475.
IEEE DOI 1910
Image segmentation, Semantics, Task analysis, Computed tomography, Shape, Medical diagnostic imaging, Image segmentation, high-resolution pathway BibRef

Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., Liu, J.,
CE-Net: Context Encoder Network for 2D Medical Image Segmentation,
MedImg(38), No. 10, October 2019, pp. 2281-2292.
IEEE DOI 1910
Image segmentation, Feature extraction, Convolution, Biomedical optical imaging, Optical imaging, Computed tomography, context encoder network BibRef

Ji, X.W.[Xue-Wen], Liu, H.Q.[Hui-Qiang], Xing, Y.[Yan], Xue, Y.L.[Yan-Ling],
Quantitative evaluation on 3D fetus morphology via X-ray grating based imaging technique,
IJIST(29), No. 4, 2019, pp. 677-685.
DOI Link 1911
biomedical research, grating-based imaging, mouse fetus, phase-sensitive micro-tomography, quantitative analysis BibRef

Wachinger, C., Toews, M., Langs, G., Wells, W., Golland, P.,
Keypoint Transfer for Fast Whole-Body Segmentation,
MedImg(39), No. 2, February 2020, pp. 273-282.
IEEE DOI 2002
Training, Image segmentation, Biomedical imaging, Computed tomography, Probabilistic logic, CT BibRef

Al Zubi, S.[Shadi], Shehab, M.[Mohammed], Al-Ayyoub, M.[Mahmoud], Jararweh, Y.[Yaser], Gupta, B.[Brij],
Parallel implementation for 3D medical volume fuzzy segmentation,
PRL(130), 2020, pp. 312-318.
Elsevier DOI 2002
Fuzzy C-means, Pattern recognition, 3D segmentation, GPU, Medical imaging, 3D visualization, Image processing BibRef

Hiasa, Y.[Yuta], Otake, Y.[Yoshito], Takao, M.[Masaki], Ogawa, T.[Takeshi], Sugan, N.[Nobuhiko],
Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling,
MedImg(39), No. 4, April 2020, pp. 1030-1040.
IEEE DOI 2004
Muscles, Uncertainty, Image segmentation, Computed tomography, Measurement, Computational modeling, Bayes methods, musculoskeletal model BibRef

Yan, Z., Yang, X., Cheng, K.,
Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework,
MedImg(39), No. 6, June 2020, pp. 2176-2189.
IEEE DOI 2006
Gland instance segmentation, segment-level shape similarity measure, adversarial learning, feature alignment BibRef

Chen, F.[Feng], Muhammad, K.[Khan], Wang, S.H.[Shui-Hua],
Three-dimensional reconstruction of CT image features based on multi-threaded deep learning calculation,
PRL(136), 2020, pp. 309-315.
Elsevier DOI 2008
Fuzzy clustering, CT image, Feature region, Deep learning, Multithreading, 3D reconstruction BibRef

Liang, S., Thung, K., Nie, D., Zhang, Y., Shen, D.,
Multi-View Spatial Aggregation Framework for Joint Localization and Segmentation of Organs at Risk in Head and Neck CT Images,
MedImg(39), No. 9, September 2020, pp. 2794-2805.
IEEE DOI 2009
Image segmentation, Computed tomography, Cancer, Optical imaging, Task analysis, head and neck cancer BibRef

Zhang, L.[Liang], Zhang, J.M.[Jia-Ming], Shen, P.Y.[Pei-Yi], Zhu, G.M.[Guang-Ming], Li, P.[Ping], Lu, X.Y.[Xiao-Yuan], Zhang, H.[Huan], Shah, S.A.[Syed Afaq], Bennamoun, M.[Mohammed],
Block Level Skip Connections Across Cascaded V-Net for Multi-Organ Segmentation,
MedImg(39), No. 9, September 2020, pp. 2782-2793.
IEEE DOI 2009
Image segmentation, Kernel, Convolution, Labeling, Cranial, Computed tomography, Task analysis, Multi-organ segmentation, hard-to-segment BibRef

Pourahmadian, F.[Fatemeh], Haddar, H.[Houssem],
Differential Tomography of Micromechanical Evolution in Elastic Materials of Unknown Micro/Macrostructure,
SIIMS(13), No. 3, 2020, pp. 1302-1330.
DOI Link 2010
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Hammami, M., Friboulet, D., Kechichian, R.,
Cycle GAN-Based Data Augmentation For Multi-Organ Detection In CT Images Via Yolo,
ICIP20(390-393)
IEEE DOI 2011
Computed tomography, Magnetic resonance imaging, Detectors, Biomedical imaging, medical imaging BibRef

Noothout, J.M.H.[Julia M. H.], de Vos, B.D.[Bob D.], Wolterink, J.M.[Jelmer M.], Postma, E.M.[Elbrich M.], Smeets, P.A.M.[Paul A. M.], Takx, R.A.P.[Richard A. P.], Leiner, T.[Tim], Viergever, M.A.[Max A.], Išgum, I.[Ivana],
Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images,
MedImg(39), No. 12, December 2020, pp. 4011-4022.
IEEE DOI 2012
Task analysis, Heating systems, Convolutional neural networks, Medical diagnostic imaging, Kernel, Head, Landmark localization, olfactory MR BibRef

Geng, M., Tian, Z., Jiang, Z., You, Y., Feng, X., Xia, Y., Yang, K., Ren, Q., Meng, X., Maier, A., Lu, Y.,
PMS-GAN: Parallel Multi-Stream Generative Adversarial Network for Multi-Material Decomposition in Spectral Computed Tomography,
MedImg(40), No. 2, February 2021, pp. 571-584.
IEEE DOI 2102
Generators, Computed tomography, Generative adversarial networks, Roads, X-ray imaging, Deep learning, Bones, Differential map, spectral X-ray imaging BibRef

Zhang, J., Xie, Y., Wang, Y., Xia, Y.,
Inter-Slice Context Residual Learning for 3D Medical Image Segmentation,
MedImg(40), No. 2, February 2021, pp. 661-672.
IEEE DOI 2102
Image segmentation, Decoding, Biomedical imaging, Tumors, Task analysis, Solid modeling, 3D medical image segmentation BibRef

Lung, K.Y.[Kuan-Yu], Chang, C.R.[Chi-Rung], Weng, S.E.[Shao-En], Lin, H.S.[Hao-Siang], Shuai, H.H.[Hong-Han], Cheng, W.H.[Wen-Huang],
ROSNet: Robust one-stage network for CT lesion detection,
PRL(144), 2021, pp. 82-88.
Elsevier DOI 2103
Deep learning, Lesion detection, Computed tomography scan, Multi-level feature pyramid, Class-balanced loss BibRef

Xue, Y.[Yi], Qin, W.J.[Wen-Jian], Luo, C.[Chen], Yang, P.F.[Peng-Fei], Jiang, Y.K.[Yang-Kang], Tsui, T.[Tiffany], He, H.J.[Hong-Jian], Wang, L.[Li], Qin, J.L.[Jia-Le], Xie, Y.Q.[Yao-Qin], Niu, T.[Tianye],
Multi-Material Decomposition for Single Energy CT Using Material Sparsity Constraint,
MedImg(40), No. 5, May 2021, pp. 1303-1318.
IEEE DOI 2105
Computed tomography, Attenuation, Phantoms, Matrix decomposition, Hospitals, STEM, Linear programming, Multi-material decomposition, two-material assumption BibRef

Tang, Y.C.[Yu-Cheng], Gao, R.[Riqiang], Han, S.Z.[Shi-Zhong], Chen, Y.Q.[Yun-Qiang], Gao, D.[Dashan], Nath, V.[Vishwesh], Bermudez, C.[Camilo], Savona, M.R.[Michael R.], Bao, S.[Shunxing], Lyu, I.[Ilwoo], Huo, Y.[Yuankai], Landman, B.A.[Bennett A.],
Body Part Regression With Self-Supervision,
MedImg(40), No. 5, May 2021, pp. 1499-1507.
IEEE DOI 2105
Computed tomography, Manuals, Unsupervised learning, Training, Navigation, Task analysis, multi-organ segmentation BibRef

Aganj, I.[Iman], Fischl, B.[Bruce],
Multi-Atlas Image Soft Segmentation via Computation of the Expected Label Value,
MedImg(40), No. 6, June 2021, pp. 1702-1710.
IEEE DOI 2106
Image segmentation, Strain, Convolution, Biomedical imaging, Computational efficiency, Training data, Training, CT BibRef

Bateson, M., Dolz, J., Kervadec, H., Lombaert, H., Ben Ayed, I.[Ismail],
Constrained Domain Adaptation for Image Segmentation,
MedImg(40), No. 7, July 2021, pp. 1875-1887.
IEEE DOI 2107
Image segmentation, Task analysis, Training, Biomedical imaging, Magnetic resonance imaging, Computed tomography, Annotations, CNN, segmentation BibRef

Perelli, A.[Alessandro], Andersen, M.S.[Martin S.],
Regularization by denoising sub-sampled Newton method for spectral CT multi-material decomposition,
Royal(A: 379), No. 2200, June 2021, pp. 20200191.
DOI Link 2107
BibRef

Lo, H.J.[Hsien-Jen], Wu, C.H.[Chih-Hung],
Local binary pattern encoding schemes for computed tomography image segmentation: An experimental and comparative study,
IJIST(31), No. 3, 2021, pp. 1300-1316.
DOI Link 2108
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WACV21(335-344)
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Computed tomography, Semantics, Pipelines, Metals, Transforms BibRef

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ICCV19(10651-10660)
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alarm systems, computerised tomography, feature extraction, image classification, image segmentation, Quality assessment BibRef

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ICCV19(10671-10680)
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biological organs, computerised tomography, gradient methods, image segmentation, medical image processing, neural nets, Neural networks BibRef

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IVCNZ19(1-5)
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biological organs, biology computing, computer vision, convolutional neural nets, image segmentation, zoology, Deep convolutional neural networks BibRef

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WACV18(547-556)
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biological organs, biomedical MRI, computerised tomography, image segmentation, learning (artificial intelligence), Training BibRef

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CRV17(124-130)
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bifurcation, computerised tomography, genetic algorithms, medical image processing, 3D anatomical trees, 3D medical images, tribes niching BibRef

Zhao, M., Miles, B., Hamarneh, G.,
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CRV17(131-138)
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Bayes methods, blood vessels, computerised tomography, feature extraction, image segmentation, medical image processing, tree structure BibRef

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IPTA12(383-390)
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ICPR14(3327-3332)
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Abdominal Seqmentation, Multi-Organ Segmentation .


Last update:Nov 30, 2021 at 22:19:38