21.8.3 Tomographic Object Construction, Object Extraction, Analysis, Organs

Herman, G.T.[Gabor T.], Rowland, S.W.[Stuart W.],
Three methods for reconstructing objects from x rays: A comparative study,
CGIP(2), No. 2, October 1973, pp. 151-178.
Elsevier DOI 0501
BibRef

Herman, G.T., Lakshminarayanan, A.V., Rowland, S.W.,
The reconstruction of objects from shadowgraphs with high contrasts,
PR(7), No. 4, December 1975, pp. 157-165.
Elsevier DOI 0309
From X-Ray shadowgraphs. Reconstruction from a limited number. BibRef

Krishnamurthy, E.V., Rao, T.M.[T. Mahadeva], Subramanian, K., Prabhu, S.S.,
Reconstruction of objects from their projections using generalized inverses,
CGIP(3), No. 4, December 1974, pp. 336-345.
Elsevier DOI 0501
BibRef

Wagner, W.,
Reconstruction of object layers from their X-ray projections: A simulation study,
CGIP(5), No. 4, December 1976, pp. 470-483.
Elsevier DOI 0501
BibRef

Schreiner, S., Dawant, B.M., Paschal, C.B., Galloway, R.L.,
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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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

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.[João 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.Y.[Jing-Yan], Noo, F.[Frederic], 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

Xu, J.Y.[Jing-Yan], Noo, F.[Frederic],
Linearized Analysis of Noise and Resolution for DL-Based Image Generation,
MedImg(42), No. 3, March 2023, pp. 647-660.
IEEE DOI 2303
Computed tomography, Image resolution, Task analysis, Noise measurement, Image reconstruction, Covariance matrices, FBPConvNet 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
BibRef

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.M., 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, 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
BibRef

Hammami, M.[Maryam], Friboulet, D.[Denis], Kechichian, R.[Razmig],
Data augmentation for multi-organ detection in medical images,
IPTA20(1-6)
IEEE DOI 2206
BibRef
And:
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, Supervised learning, Object detection, Tools, Biomedical imaging, medical imaging. Detectors. 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.Y.[Tian-Ye],
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.Q.[Ri-Qiang], Han, S.Z.[Shi-Zhong], Chen, Y.Q.[Yun-Qiang], Gao, D.S.[Da-Shan], 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
clustering, CT images, Euclidean distance, fuzzy C-means, image segmentation, local binary pattern BibRef

Yan, K.[Ke], Cai, J.Z.[Jin-Zheng], Zheng, Y.J.[You-Jing], Harrison, A.P.[Adam P.], Jin, D.[Dakai], Tang, Y.B.[You-Bao], Tang, Y.X.[Yu-Xing], Huang, L.Y.[Ling-Yun], Xiao, J.[Jing], Lu, L.[Le],
Learning From Multiple Datasets With Heterogeneous and Partial Labels for Universal Lesion Detection in CT,
MedImg(40), No. 10, October 2021, pp. 2759-2770.
IEEE DOI 2110
Lesions, Annotations, Training, Lenses, Proposals, Computed tomography, Task analysis, Lesion detection, multi-dataset learning, multi-task learning BibRef

Han, X.T.[Xiao-Ting], Qi, L.[Lei], Yu, Q.[Qian], Zhou, Z.Q.[Zi-Qi], Zheng, Y.F.[Ye-Feng], Shi, Y.H.[Ying-Huan], Gao, Y.[Yang],
Deep Symmetric Adaptation Network for Cross-Modality Medical Image Segmentation,
MedImg(41), No. 1, January 2022, pp. 121-132.
IEEE DOI 2201
Image segmentation, Computed tomography, Decoding, Biomedical imaging, Semantics, Magnetic resonance imaging, deep symmetric architecture BibRef

Yu, Q.[Qian], Qi, L.[Lei], Gao, Y.[Yang], Wang, W.Z.[Wu-Zhang], Shi, Y.H.[Ying-Huan],
Crosslink-Net: Double-Branch Encoder Network via Fusing Vertical and Horizontal Convolutions for Medical Image Segmentation,
IP(31), 2022, pp. 5893-5908.
IEEE DOI 2209
Kernel, Image segmentation, Biomedical imaging, Shape, Decoding, Context modeling, Computer architecture, Double-branch encoder, segmentation BibRef

Huang, B.[Bin], Ye, Y.F.[Yu-Feng], Xu, Z.Y.[Zi-Yue], Cai, Z.Y.[Zong-You], He, Y.[Yan], Zhong, Z.N.[Zhang-Nan], Liu, L.X.[Ling-Xiang], Chen, X.[Xin], Chen, H.W.[Han-Wei], Huang, B.S.[Bing-Sheng],
3D Lightweight Network for Simultaneous Registration and Segmentation of Organs-at-Risk in CT Images of Head and Neck Cancer,
MedImg(41), No. 4, April 2022, pp. 951-964.
IEEE DOI 2204
Image segmentation, Computed tomography, Cancer, Pipelines, Shape, Neck, Segmentation, registration, computed tomography, organ-at-risk, lightweight network BibRef

Zhou, Y.Y.[Yu-Yin], Dreizin, D.[David], Wang, Y.[Yan], Liu, F.Z.[Feng-Ze], Shen, W.[Wei], Yuille, A.L.[Alan L.],
External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation With Limited Data,
MedImg(41), No. 6, June 2022, pp. 1346-1357.
IEEE DOI 2206
Injuries, Image segmentation, Training, Computed tomography, Annotations, Imaging, Data mining, attention BibRef

Francis, S.[Seenia], Pooloth, G.[Goutham], Singam, S.B.S.[Sai Bala Subrahmanyam], Puzhakkal, N.[Niyas], Narayanan, P.P.[Pournami Pulinthanathu], Balakrishnan, J.P.[Jayaraj Pottekkattuvalappil],
SABOS-Net: Self-supervised attention based network for automatic organ segmentation of head and neck CT images,
IJIST(33), No. 1, 2023, pp. 175-191.
DOI Link 2301
auto-contouring, deep learning, head and neck CT, organs at risk(OAR), radiation therapy, residual U-net, self supervision BibRef

Zhong, Z.Q.[Zhi-Qiang], He, L.[Lian], Chen, C.X.[Chang-Xiu], Yang, X.[Xingli], Lin, L.[Li], Yan, Z.[Ziye], Tian, M.Q.[Meng-Qiu], Sun, Y.[Ying], Zhan, Y.W.[Yin-Wei],
Full-scale attention network for automated organ segmentation on head and neck CT and MR images,
IET-IPR(17), No. 3, 2023, pp. 660-673.
DOI Link 2303
BibRef

Yan, Q.S.[Qing-Sen], Liu, S.Q.[Sheng-Qiang], Xu, S.H.[Song-Hua], Dong, C.X.[Cai-Xia], Li, Z.F.[Zong-Fang], Shi, J.Q.F.[Javen Qin-Feng], Zhang, Y.N.[Yan-Ning], Dai, D.[Duwei],
3D Medical image segmentation using parallel transformers,
PR(138), 2023, pp. 109432.
Elsevier DOI 2303

WWW Link. 3D Medical image segmentation, Deep learning, Transformers, Attention, Fusion, High-resolution representations, Low-resolution representations BibRef

Chen, M.J.[Mei-Juan], Zhuo, L.[Li], Zhu, Z.[Ziyao], Yin, H.X.[Hong-Xia], Li, X.G.[Xiao-Guang], Wang, Z.C.[Zhen-Chang],
Deeply supervised vestibule segmentation network for CT images with global context-aware pyramid feature extraction,
IET-IPR(17), No. 4, 2023, pp. 1267-1279.
DOI Link 2303
active contour with elastic (ACE) loss, deep supervision, global context-aware pyramid feature extraction, vestibule segmentation BibRef

Wang, S.F.[Shao-Fan], Liu, Y.K.[Yu-Kun], Sun, Y.F.[Yan-Feng], Yin, B.C.[Bao-Cai],
SACNet: Shuffling atrous convolutional U-Net for medical image segmentation,
IET-IPR(17), No. 4, 2023, pp. 1236-1252.
DOI Link 2303
convolutional neural nets, medical image processing BibRef

Wang, P.[Ping], Peng, J.Z.[Ji-Zong], Pedersoli, M.[Marco], Zhou, Y.F.[Yuan-Feng], Zhang, C.M.[Cai-Ming], Desrosiers, C.[Christian],
CAT: Constrained Adversarial Training for Anatomically-Plausible Semi-Supervised Segmentation,
MedImg(42), No. 8, August 2023, pp. 2146-2161.
IEEE DOI 2308
Image segmentation, Training, Shape, Software, Task analysis, Deep learning, Biomedical imaging, Medical image segmentation, reinforcement learning BibRef

Yuan, F.N.[Fei-Niu], Tang, Z.D.[Zhao-Da], Wang, C.M.[Chun-Mei], Huang, Q.H.[Qing-Hua], Shi, J.T.[Jin-Ting],
A multiple gated boosting network for multi-organ medical image segmentation,
IET-IPR(17), No. 10, 2023, pp. 3028-3039.
DOI Link 2308
medical image processing, transforms BibRef

Zhao, Q.F.[Qian-Fei], Zhong, L.F.[Lan-Feng], Xiao, J.H.[Jiang-Hong], Zhang, J.B.[Jing-Bo], Chen, Y.[Yinan], Liao, W.J.[Wen-Jun], Zhang, S.T.[Shao-Ting], Wang, G.[Guotai],
Efficient Multi-Organ Segmentation From 3D Abdominal CT Images With Lightweight Network and Knowledge Distillation,
MedImg(42), No. 9, September 2023, pp. 2513-2523.
IEEE DOI 2310
BibRef

Pandey, P.[Prashant], Chasmai, M.[Mustafa], Sur, T.[Tanuj], Lall, B.[Brejesh],
Robust Prototypical Few-Shot Organ Segmentation With Regularized Neural-ODEs,
MedImg(42), No. 9, September 2023, pp. 2490-2501.
IEEE DOI 2310
BibRef

Xu, X.[Xuanang], Deng, H.H.[Hannah H.], Gateno, J.[Jamie], Yan, P.K.[Ping-Kun],
Federated Multi-Organ Segmentation With Inconsistent Labels,
MedImg(42), No. 10, October 2023, pp. 2948-2960.
IEEE DOI 2310
BibRef

Francis, S.[Seenia], Jayaraj, P.B., Pournami, P.N., Puzhakkal, N.[Niyas],
ContourGAN: Auto-contouring of organs at risk in abdomen computed tomography images using generative adversarial network,
IJIST(33), No. 5, 2023, pp. 1494-1504.
DOI Link 2310
abdomen CT, auto-contouring, deep learning, generative models, OAR segmentation, radiation therapy, UNet BibRef

Liu, H.[Han], Xu, Z.B.[Zhou-Bing], Gao, R.Q.[Ri-Qiang], Li, H.[Hao], Wang, J.N.[Jia-Ning], Chabin, G.[Guillaume], Oguz, I.[Ipek], Grbic, S.[Sasa],
COSST: Multi-Organ Segmentation With Partially Labeled Datasets Using Comprehensive Supervisions and Self-Training,
MedImg(43), No. 5, May 2024, pp. 1995-2009.
IEEE DOI 2405
Task analysis, Training, Image segmentation, Biological systems, Annotations, Computed tomography, Biomedical imaging, pseudo label BibRef

Song, H.F.[Hao-Fei], Mao, X.T.[Xin-Tian], Yu, J.[Jing], Li, Q.L.[Qing-Li], Wang, Y.[Yan],
I³Net: Inter-Intra-Slice Interpolation Network for Medical Slice Synthesis,
MedImg(43), No. 9, September 2024, pp. 3306-3318.
IEEE DOI Code:
WWW Link. 2409
Interpolation, Superresolution, Task analysis, Frequency-domain analysis, Computed tomography, slice-wise interpolation BibRef

Chen, Y.X.[Yi-Xin], Gao, Y.[Yajuan], Zhu, L.[Lei], Shao, W.R.[Wen-Rui], Lu, Y.[Yanye], Han, H.B.[Hong-Bin], Xie, Z.H.[Zhao-Heng],
PCNet: Prior Category Network for CT Universal Segmentation Model,
MedImg(43), No. 9, September 2024, pp. 3319-3330.
IEEE DOI Code:
WWW Link. 2409
Image segmentation, Biomedical imaging, Task analysis, Computational modeling, Computed tomography, prompt BibRef

Shaker, A.[Abdelrahman], Maaz, M.[Muhammad], Rasheed, H.[Hanoona], Khan, S.[Salman], Yang, M.H.[Ming-Hsuan], Khan, F.S.[Fahad Shahbaz],
UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation,
MedImg(43), No. 9, September 2024, pp. 3377-3390.
IEEE DOI Code:
WWW Link. 2409
Image segmentation, Transformers, Biomedical imaging, Complexity theory, Graphics processing units, medical image segmentation BibRef

Geng, H.X.[Hai-Xiao], Fan, J.F.[Jing-Fan], Yang, S.[Shuo], Chen, S.[Sigeng], Xiao, D.Q.[De-Qiang], Ai, D.[Danni], Fu, T.Y.[Tian-Yu], Song, H.[Hong], Yuan, K.[Kai], Duan, F.[Feng], Wang, Y.T.[Yong-Tian], Yang, J.[Jian],
DSC-Recon: Dual-Stage Complementary 4-D Organ Reconstruction From X-Ray Image Sequence for Intraoperative Fusion,
MedImg(43), No. 11, November 2024, pp. 3909-3923.
IEEE DOI 2411
X-ray imaging, Shape, Image reconstruction, Computed tomography, Interpolation, Deformation, Organ reconstruction, X-ray image sequence BibRef

Xu, Y.[Yanwu], Sun, L.[Li], Peng, W.[Wei], Jia, S.[Shuyue], Morrison, K.[Katelyn], Perer, A.[Adam], Zandifar, A.[Afrooz], Visweswaran, S.[Shyam], Eslami, M.[Motahhare], Batmanghelich, K.[Kayhan],
MedSyn: Text-Guided Anatomy-Aware Synthesis of High-Fidelity 3-D CT Images,
MedImg(43), No. 10, October 2024, pp. 3648-3660.
IEEE DOI 2411
Biomedical imaging, Radiology, Computed tomography, Image synthesis, Atmospheric modeling, Lung, Diffusion model, controllable synthesis BibRef

Liu, T.[Tao], Zhang, X.[Xukun], Yang, Z.[Zhongwei], Han, M.H.[Ming-Hao], Kuang, H.[Haopeng], Ma, S.W.[Shu-Wei], Wang, L.[Le], Wang, X.Y.[Xiao-Ying], Zhang, L.H.[Li-Hua],
Dual knowledge-guided two-stage model for precise small organ segmentation in abdominal CT images,
IET-IPR(18), No. 13, 2024, pp. 3935-3949.
DOI Link 2411
computerised tomography, learning (artificial intelligence), medical image processing BibRef

Ye, Y.W.[Yi-Wen], Zhang, J.P.[Jian-Peng], Chen, Z.Y.[Zi-Yang], Xia, Y.[Yong],
CADS: A Self-Supervised Learner via Cross-Modal Alignment and Deep Self-Distillation for CT Volume Segmentation,
MedImg(44), No. 1, January 2025, pp. 118-129.
IEEE DOI Code:
WWW Link. 2501
Computed tomography, Task analysis, X-ray imaging, Image segmentation, Contrastive learning, Solid modeling, CT volume segmentation BibRef

Xie, W.Y.[Wei-Yi], Willems, N.[Nathalie], Patil, S.[Shubham], Li, Y.[Yang], Kumar, M.[Mayank],
SAM Fewshot Finetuning for Anatomical Segmentation in Medical Images,
WACV24(3241-3249)
IEEE DOI 2404
Training, Image segmentation, Decoding, Labeling, Task analysis, Biomedical imaging, Algorithms, 3D Applications, Biomedical / healthcare / medicine BibRef

Bhattacharya, S.[Samayan], Bhattacharya, A.[Avigyan], Shahnawaz, S.[Sk],
Generating Synthetic Computed Tomography (CT) Images to Improve the Performance of Machine Learning Model for Pediatric Abdominal Anomaly Detection,
BioIm23(3867-3875)
IEEE DOI 2401
BibRef

Li, Z.L.[Zi-Long], Ma, C.L.[Cheng-Long], Chen, J.[Jie], Zhang, J.P.[Jun-Ping], Shan, H.M.[Hong-Ming],
Learning to Distill Global Representation for Sparse-View CT,
ICCV23(21139-21150)
IEEE DOI Code:
WWW Link. 2401
BibRef

Ji, Z.H.X.[Zhang-He-Xuan], Guo, D.[Dazhou], Wang, P.[Puyang], Yan, K.[Ke], Lu, L.[Le], Xu, M.F.[Min-Feng], Wang, Q.F.[Qi-Feng], Ge, J.[Jia], Gao, M.C.[Ming-Chen], Ye, X.H.[Xiang-Hua], Jin, D.[Dakai],
Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans,
ICCV23(21083-21094)
IEEE DOI 2401
BibRef

Wang, S.Q.[Si-Qi], Yatagawa, T.[Tatsuya], Ohtake, Y.[Yutaka], Aoki, T.[Toru], Hotta, J.[Jun],
End-to-End Deep Learning for Reconstructing Segmented 3D CT Image from Multi-Energy X-ray Projections,
CVAMD23(2566-2574)
IEEE DOI 2401
BibRef

El Jurdi, R.[Rosana], Dargent, T.[Thomas], Petitjean, C.[Caroline], Honeine, P.[Paul], Abdallah, F.[Fahed],
Investigating CoordConv for Fully and Weakly Supervised Medical Image Segmentation,
IPTA20(1-5)
IEEE DOI 2206
Image segmentation, Convolution, Computed tomography, Tools, Convolutional neural networks, Task analysis, Biomedical imaging, CT BibRef

Renders, J.[Jens], de Beenhouwer, J.[Jan], Sijbers, J.[Jan],
Mesh-Based Reconstruction of Dynamic Foam Images Using X-Ray CT,
3DV21(1312-1320)
IEEE DOI 2201
Visualization, Solid modeling, Computed tomography, Synchrotrons, Memory management, Reconstruction algorithms, 4D CT, Tomography, Foam BibRef

Tekawade, A.[Aniket], Liu, Z.C.[Zheng-Chun], Kenesei, P.[Peter], Bicer, T.[Tekin], de Carlo, F.[Francesco], Kettimuthu, R.[Rajkumar], Foster, I.[Ian],
3d Autoencoders for Feature Extraction In X-Ray Tomography,
ICIP21(3477-3481)
IEEE DOI 2201
Image segmentation, Absorption, Volume measurement, X-ray tomography, Tomography, Feature extraction, porosity BibRef

Yang, A.[Anqi], Pan, F.[Feng], Saragadam, V.[Vishwanath], Dao, D.[Duy], Hui, Z.[Zhuo], Chang, J.H.R.[Jen-Hao Rick], Sankaranarayanan, A.C.[Aswin C.],
SliceNets: A Scalable Approach for Object Detection in 3D CT Scans,
WACV21(335-344)
IEEE DOI 2106
Training, Image segmentation, Solid modeling, Computed tomography, Weapons, Neural networks BibRef

Tang, H.[Hao], Liu, X.W.[Xing-Wei], Han, K.[Kun], Xie, X.H.[Xiao-Hui], Chen, X.M.[Xu-Ming], Qian, H.[Huang], Liu, Y.[Yong], Sun, S.[Shanlin], Bai, N.[Narisu],
Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation,
WACV21(938-948)
IEEE DOI 2106
Image segmentation, Solid modeling, Image analysis, Computed tomography, Magnetic resonance imaging, Information filters BibRef

Hati, A.[Avik], Bustreo, M.[Matteo], Sona, D.[Diego], Murino, V.[Vittorio], del Bue, A.[Alessio],
Weakly Supervised Geodesic Segmentation of Egyptian Mummy CT Scans,
ICPR21(5565-5572)
IEEE DOI 2105
Computed tomography, Semantics, Pipelines, Metals, Transforms BibRef

Lauze, F.[François], Quéau, Y.[Yvain], Plenge, E.[Esben],
Simultaneous Reconstruction and Segmentation of CT Scans with Shadowed Data,
SSVM17(308-319).
Springer DOI 1706
BibRef

Liu, F.Z.[Feng-Ze], Xia, Y.D.[Ying-Da], Yang, D.[Dong], Yuille, A.L.[Alan L.], Xu, D.G.[Da-Guang],
An Alarm System for Segmentation Algorithm Based on Shape Model,
ICCV19(10651-10660)
IEEE DOI 2004
alarm systems, computerised tomography, feature extraction, image classification, image segmentation, Quality assessment BibRef

Zhou, Y.Y.[Yu-Yin], Li, Z.[Zhe], Bai, S.[Song], Chen, X.L.[Xin-Lei], Han, M.[Mei], Wang, C.[Chong], Fishman, E.[Elliot], Yuille, A.L.[Alan L.],
Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation,
ICCV19(10671-10680)
IEEE DOI 2004
biological organs, computerised tomography, gradient methods, image segmentation, medical image processing, neural nets, Neural networks BibRef

Hassan, S.I., Stommel, M., Lowe, A., Zhang, Q., Xu, W.,
Semantic Segmentation of Sheep Organs by Convolutional Neural Networks,
IVCNZ19(1-5)
IEEE DOI 2004
biological organs, biology computing, convolutional neural nets, image segmentation, zoology, Deep convolutional neural networks BibRef

Danilov, V.V., Skirnevskiy, I.P., Manakov, R.A., Kolpashchikov, D.Y., Gerget, O.M., Frangi, A.F.,
Ray-based Segmentation Algorithm for Medical Imaging,
PTVSBB19(37-45).
DOI Link 1912
BibRef

Javaid, U.[Umair], Dasnoy, D.[Damien], Lee, J.A.[John A.],
Multi-organ Segmentation of Chest CT Images in Radiation Oncology: Comparison of Standard and Dilated UNet,
ACIVS18(188-199).
Springer DOI 1810
BibRef

Léger, J.[Jean], Brion, E.[Eliott], Javaid, U.[Umair], Lee, J.[John], de Vleeschouwer, C.[Christophe], Macq, B.[Benoit],
Contour Propagation in CT Scans with Convolutional Neural Networks,
ACIVS18(380-391).
Springer DOI 1810
BibRef

Valindria, V.V., Pawlowski, N., Rajchl, M., Lavdas, I., Aboagye, E.O., Rockall, A.G., Rueckert, D., Glocker, B.,
Multi-modal Learning from Unpaired Images: Application to Multi-organ Segmentation in CT and MRI,
WACV18(547-556)
IEEE DOI 1806
biological organs, biomedical MRI, computerised tomography, image segmentation, learning (artificial intelligence), Training BibRef

Zhao, M., Hamarneh, G.,
Bifurcation Localization in 3D Images via Evolutionary Geometric Deformable Templates,
CRV17(124-130)
IEEE DOI 1804
bifurcation, computerised tomography, genetic algorithms, medical image processing, 3D anatomical trees, 3D medical images, tribes niching BibRef

Zhao, M., Miles, B., Hamarneh, G.,
Leveraging Tree Statistics for Extracting Anatomical Trees from 3D Medical Images,
CRV17(131-138)
IEEE DOI 1804
Bayes methods, blood vessels, computerised tomography, feature extraction, image segmentation, medical image processing, tree structure BibRef

Kadu, A.[Ajinkya], van Leeuwen, T.[Tristan], Batenburg, K.J.[K. Joost],
A Parametric Level-Set Method for Partially Discrete Tomography,
DGCI17(122-134).
Springer DOI 1711
BibRef

Alvén, J.[Jennifer], Kahl, F.[Fredrik], Landgren, M.[Matilda], Larsson, V.[Viktor], Ulén, J.[Johannes],
Shape-aware multi-atlas segmentation,
ICPR16(1101-1106)
IEEE DOI 1705
Image segmentation, Imaging, Robustness, Shape, Standards, Training BibRef

Wang, L.[Li], Gao, Y.Z.[Yao-Zong], Shi, F.[Feng], Li, G.[Gang], Chen, K.C.[Ken-Chung], Tang, Z.[Zhen], Xia, J.J.[James J.], Shen, D.G.[Ding-Gang],
Automated Segmentation of CBCT Image with Prior-Guided Sequential Random Forest,
MCV15(72-82).
Springer DOI 1608
BibRef

Yamada, M.[Mitsunori], Hontani, H.[Hidekata], Matsuzoe, H.[Hiroshi],
A Study on Model Selection from the q-Exponential Distribution for Constructing an Organ Point Distribution Model,
MCBMIIA15(258-269).
Springer DOI 1603
BibRef

Kim, H., Thiagarajan, J.J., Bremer, P.T.,
A Randomized Ensemble Approach to Industrial CT Segmentation,
ICCV15(1707-1715)
IEEE DOI 1602
Computed tomography BibRef

Okagawa, A.[Asuka], Oyamada, Y.J.[Yu-Ji], Mochizuki, Y.[Yoshihiko], Ishikawa, H.[Hiroshi],
Multi-organ segmentation by minimization of higher-order energy for CT boundary,
MVA15(547-550)
IEEE DOI 1507
Biomedical imaging BibRef

Takaoka, T., Mochizuki, Y.[Yoshihiko], Ishikawa, H.[Hiroshi],
Multiple-organ segmentation by graph cuts with supervoxel nodes,
MVA17(424-427)
DOI Link 1708
Biomedical imaging, Computed tomography, Image segmentation, Labeling, Minimization, Object segmentation, Three-dimensional, displays BibRef

Morita, M.[Minato], Okagawa, A.[Asuka], Oyamada, Y.J.[Yu-Ji], Mochizuki, Y.[Yoshihiko], Ishikawa, H.[Hiroshi],
Multiple-organ segmentation based on spatially-divided neighboring data energy,
MVA15(158-161)
IEEE DOI 1507
Biomedical imaging BibRef

Keatmanee, C.[Chadaporn], Makhanov, S.S.[Stanislav S.], Kotani, K.[Kazunori], Kondo, T.[Toshiaki], Thongvigitmanee, S.S.[Saowapak S.],
Inferior alveolar canal segmentation in cone beam computed tomography images using an adaptive diffusion flow active contour model,
MVA15(57-60)
IEEE DOI 1507
Active contours BibRef

Boulemnadjel, A., Hachouf, F.[Fella],
A new method for finding clusters embedded in subspaces applied to medical tomography scan image,
IPTA12(383-390)
IEEE DOI 1503
computerised tomography BibRef

Wang, C.L.[Chun-Liang], Smedby, O.[Orjan],
Automatic Multi-organ Segmentation in Non-enhanced CT Datasets Using Hierarchical Shape Priors,
ICPR14(3327-3332)
IEEE DOI 1412
Biomedical imaging BibRef

Kamencay, P., Zachariasova, M., Hudec, R., Benco, M., Radil, R.,
3D image reconstruction from 2D CT slices,
3DTV-CON14(1-4)
IEEE DOI 1409
computerised tomography BibRef

Bateman, C.J., McMahon, J., Malpas, A., de Ruiter, N., Bell, S., Butler, A.P., Butler, P.H., Renaud, P.F.,
Segmentation enhances material analysis in multi-energy CT: A simulation study,
IVCNZ13(190-195)
IEEE DOI 1402
computerised tomography BibRef

Chen, J., Millane, R.,
Diffraction by small crystals with incomplete unit cells,
IVCNZ13(65-69)
IEEE DOI 1402
X-ray crystallography BibRef

Li, S.[Shuai], Zhao, Q.P.[Qin-Ping], Wang, S.F.[Sheng-Fa], Hao, A.[Aimin], Qin, H.[Hong],
Multi-scale, multi-level, heterogeneous features extraction and classification of volumetric medical images,
ICIP13(1418-1422)
IEEE DOI 1402
CUDA BibRef

Dang, K.[Kang], Yuan, J.S.[Jun-Song], Tiong, H.Y.[Ho Yee],
Voxel labelling in CT images with data-driven contextual features,
ICIP13(680-684)
IEEE DOI 1402
Biomedical imaging BibRef

Weinlich, A.[Andreas], Amon, P.[Peter], Hutter, A.[Andreas], Kaup, A.[Andre],
Edge modeling prediction for computed tomography images,
VCIP12(1-6).
IEEE DOI 1302
BibRef

Vantaram, S.R.[Sreenath Rao], Saber, E.[Eli], Dianat, S.A.[Sohail A.], Hu, Y.[Yang], Abhyankar, V.[Vishwas],
Semi-automatic 3-D segmentation of Computed Tomographic imagery by iterative gradient-driven volume growing,
ICIP11(2857-2860).
IEEE DOI 1201
BibRef

Yang, W.[Wei], Wang, X.L.[Xiao-Long], Lin, L.[Liang], Gao, C.Y.[Cheng-Ying],
Interactive CT image segmentation with online discriminative learning,
ICIP11(425-428).
IEEE DOI 1201
BibRef

Bhole, C.[Chetan], Morsillo, N.[Nicholas], Pal, C.[Christopher],
3D Segmentation in CT Imagery with Conditional Random Fields and Histograms of Oriented Gradients,
MLMI11(326-334).
Springer DOI 1109
BibRef

Guedon, J.P.[Jean-Pierre], Liu, C.L.[Chuan-Lin],
The 2 and 3 materials scene reconstructed from some line Mojette projections,
IPTA10(189-194).
IEEE DOI 1007
Mojette projection for more images in tomography reconstruction. BibRef

Ouzounis, G.K.[Georgios K.], Giannakopoulos, S.[Stilianos], Simopoulos, C.E.[Constantinos E.], Wilkinson, M.H.F.[Michael H.F.],
Robust extraction of urinary stones from CT data using attribute filters,
ICIP09(2629-2632).
IEEE DOI 0911
BibRef

Gaullier, G.[Gil], Charbonnier, P.[Pierre], Heitz, F.[Fabrice],
Introducing shape priors in object-based tomographic reconstruction,
ICIP09(1077-1080).
IEEE DOI 0911
BibRef

Yang, H.F.[Huei-Fang], Choe, Y.S.[Yoon-Suck],
3D volume extraction of densely packed cells in EM data stack by forward and backward graph cuts,
CIMSVP09(47-52).
IEEE DOI 0903
BibRef

Shinohara, T.,
Segmentation of Intertwining Stringlike Objects in Three Dimensional CT Image Based on Positional Information,
IMVIP09(30-35).
IEEE DOI 0909
BibRef

Lagrange, J., Dauty, I., and Azencott, R.,
Tomographic Reconstruction of Axisymmetrical Objects From One View by Model Approximation,
ICIP97(I: 492-495).
IEEE DOI BibRef 9700

Hohne, K.H., Bomans, M., Pommert, A., Riemer, M., Tiede, U.,
3D-segmentation and display of tomographic imagery,
ICPR88(II: 1271-1276).
IEEE DOI 8811
BibRef

Magnusson, M., Lenz, R., Danielsson, P.E.,
Evaluation of methods for shaded surface display of CT-volumes,
ICPR88(II: 1287-1294).
IEEE DOI 8811
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Abdominal Seqmentation, Multi-Organ Segmentation .