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IEEE DOI
2307
Transformers, Data structures, Boolean functions,
Image segmentation, volumetric image segmentation
BibRef
Zhai, S.W.[Shu-Wei],
Wang, G.T.[Guo-Tai],
Luo, X.D.[Xiang-De],
Yue, Q.[Qiang],
Li, K.[Kang],
Zhang, S.T.[Shao-Ting],
PA-Seg: Learning From Point Annotations for 3D Medical Image
Segmentation Using Contextual Regularization and Cross Knowledge
Distillation,
MedImg(42), No. 8, August 2023, pp. 2235-2246.
IEEE DOI
2308
Annotations, Image segmentation, Training, Noise measurement,
Solid modeling, Costs, Medical image segmentation,
knowledge distillation
BibRef
Yang, J.Y.[Jing-Yi],
Jiao, L.C.[Li-Cheng],
Shang, R.H.[Rong-Hua],
Liu, X.[Xu],
Li, R.Y.[Rui-Yang],
Xu, L.C.[Long-Chang],
EPT-Net: Edge Perception Transformer for 3D Medical Image
Segmentation,
MedImg(42), No. 11, November 2023, pp. 3229-3243.
IEEE DOI
2311
BibRef
Tadokoro, R.[Ryu],
Yamada, R.[Ryosuke],
Kataoka, H.[Hirokatsu],
Pre-training Auto-generated Volumetric Shapes for 3D Medical Image
Segmentation,
ECV23(4740-4745)
IEEE DOI
2309
BibRef
Peters, J.[Joshua],
Lebrat, L.[Léo],
Cruz, R.S.[Rodrigo Santa],
Nicolson, A.[Aaron],
Belous, G.[Gregg],
Konate, S.[Salamata],
Raniga, P.[Parnesh],
Dore, V.[Vincent],
Bourgeat, P.[Pierrick],
Mejan-Fripp, J.[Jurgen],
Fookes, C.[Clinton],
Salvado, O.[Olivier],
DBCE: A Saliency Method for Medical Deep Learning Through
Anatomically-Consistent Free-Form Deformations,
WACV23(1958-1968)
IEEE DOI
2302
Deformable models, Deep learning, Atrophy, Solid modeling,
Training data, Medical services, Algorithms: Explainable, fair,
Biomedical/healthcare/medicine
BibRef
Zhang, K.[Ke],
Zhuang, X.[Xiahai],
CycleMix: A Holistic Strategy for Medical Image Segmentation from
Scribble Supervision,
CVPR22(11646-11655)
IEEE DOI
2210
Training, Image segmentation, Codes, Annotations,
Computational modeling, Training data, Segmentation,
Self- semi- meta- unsupervised learning
BibRef
Yang, J.C.[Jian-Cheng],
Wickramasinghe, U.[Udaranga],
Ni, B.B.[Bing-Bing],
Fua, P.[Pascal],
ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging,
CVPR22(15840-15850)
IEEE DOI
2210
Training, Shape, Pipelines, Sensors, Pattern recognition,
3D from multi-view and sensors, Medical,
grouping and shape analysis
BibRef
Guo, H.T.[Heng-Tao],
Planche, B.[Benjamin],
Zheng, M.[Meng],
Karanam, S.[Srikrishna],
Chen, T.[Terrence],
Wu, Z.Y.[Zi-Yan],
SMPL-A: Modeling Person-Specific Deformable Anatomy,
CVPR22(20782-20791)
IEEE DOI
2210
Deformable models, Training, Point cloud compression, In vivo,
Protocols, Shape, Surgery, Medical, biological and cell microscopy,
Pose estimation and tracking
BibRef
Han, K.[Kun],
Sun, S.[Shanlin],
Le, T.T.[Thanh-Tung],
Yan, X.Y.[Xiang-Yi],
Ma, H.Y.[Hao-Yu],
You, C.Y.[Chen-Yu],
Xie, X.H.[Xiao-Hui],
Hybrid Neural Diffeomorphic Flow for Shape Representation and
Generation via Triplane,
WACV24(7692-7702)
IEEE DOI
2404
Solid modeling, Image segmentation, Limiting, Shape, Deformation,
Applications, Biomedical / healthcare / medicine, Algorithms, 3D computer vision
BibRef
Sun, S.[Shanlin],
Han, K.[Kun],
Kong, D.Y.[De-Ying],
Tang, H.[Hao],
Yan, X.Y.[Xiang-Yi],
Xie, X.H.[Xiao-Hui],
Topology-Preserving Shape Reconstruction and Registration via Neural
Diffeomorphic Flow,
CVPR22(20813-20823)
IEEE DOI
2210
Deformable models, Geometry, Shape, Computational modeling,
Semantics, Topology, Medical, biological and cell microscopy,
grouping and shape analysis
BibRef
Makki, K.[Karim],
Bohi, A.[Amine],
Ogier, A.C.[Augustin C.],
Bellemare, M.E.[Marc Emmanuel],
A new geodesic-based feature for characterization of 3D shapes:
application to soft tissue organ temporal deformations,
ICPR21(3822-3828)
IEEE DOI
2105
Shape, Tracking,
Magnetic resonance imaging, Biological systems, Bladder,
high-resolution reconstruction
BibRef
Lan, S.[Sheng],
Guo, Z.H.[Zhen-Hua],
A Joint Super-Resolution and Deformable Registration Network for 3D
Brain Images,
ICPR21(173-179)
IEEE DOI
2105
Image registration, Brain, Instruments,
Superresolution, Pattern recognition, Task analysis
BibRef
Comelli, A.[Albert],
Stefano, A.[Alessandro],
Active Surface for Fully 3d Automatic Segmentation,
AIHA20(357-367).
Springer DOI
2103
BibRef
Zhu, Z.,
Xia, Y.,
Shen, W.,
Fishman, E.,
Yuille, A.L.[Alan L.],
A 3D Coarse-to-Fine Framework for Volumetric Medical Image
Segmentation,
3DV18(682-690)
IEEE DOI
1812
biological organs, convolution, feedforward neural nets,
image segmentation, medical image processing,
3D CNNs
BibRef
Dalca, A.V.,
Guttag, J.,
Sabuncu, M.R.,
Anatomical Priors in Convolutional Networks for Unsupervised
Biomedical Segmentation,
CVPR18(9290-9299)
IEEE DOI
1812
Image segmentation, Biological system modeling,
Biomedical imaging, Convolutional neural networks, Shape,
Decoding
BibRef
Tward, D.J.[Daniel J.],
Sicat, C.S.[Chelsea S.],
Brown, T.[Timothy],
Bakker, A.[Arnold],
Miller, M.I.[Michael I.],
Reducing Variability in Anatomical Definitions Over Time Using
Longitudinal Diffeomorphic Mapping,
SeSAME16(51-62).
Springer DOI
1703
BibRef
Sagawa, R.[Ryusuke],
Yoshiyasu, Y.[Yusuke],
Alspach, A.[Alexander],
Ayusawa, K.[Ko],
Yamane, K.[Katsu],
Hilton, A.[Adrian],
Analyzing Muscle Activity and Force with Skin Shape Captured by
Non-contact Visual Sensor,
PSIVT15(488-501).
Springer DOI
1602
BibRef
Kim, S.Y.[Sun-Yeong],
Tai, Y.W.[Yu-Wing],
Hierarchical nonrigid model for 3D medical image registration,
ICIP14(3562-3566)
IEEE DOI
1502
Equations
BibRef
Rolfe, S.M.[Sara M.],
Cox, L.L.,
Shapiro, L.G.,
Cox, T.C.,
A New Landmark-Independent Tool for Quantifying and Characterizing
Morphologic Variation,
ICIAR14(I: 75-83).
Springer DOI
1410
deformable-registration-based
BibRef
Wassermann, D.[Demian],
Ross, J.[James],
Washko, G.R.[George R.],
Wells, W.M.[William M.],
Jose-Estepar, R.S.[Raul San],
Deformable Registration of Feature-Endowed Point Sets Based on Tensor
Fields,
CVPR14(2729-2735)
IEEE DOI
1409
Diffeomorphism; Medical Imaging; Registration; Tensor Field
BibRef
Inagaki, S.[Shun],
Itoh, H.[Hayato],
Imiya, A.[Atsushi],
Variational Multiple Warping for Cardiac Image Analysis,
CAIP15(II:749-759).
Springer DOI
1511
BibRef
And:
Multiple Alignment of Spatiotemporal Deformable Objects for the
Average-Organ Computation,
NORDIA14(353-366).
Springer DOI
1504
See also Dimension Reduction and Construction of Feature Space for Image Pattern Recognition.
BibRef
Inagaki, S.[Shun],
Imiya, A.[Atsushi],
Variational Deformation Method for the Computation of the Average
Shape of Organs,
Registration14(343-350)
IEEE DOI
1409
BibRef
Bennett, S.[Stuart],
Lasenby, J.[Joan],
Deformation Measurement of a Human Chest Experiencing Global Motion,
AMDO12(144-152).
Springer DOI
1208
BibRef
Flores-Mangas, F.[Fernando],
Jepson, A.D.[Allan D.],
Haider, M.A.[Masoom A.],
Shape-Based Registration of Kidneys Across Differently Contrasted CT
Scans,
CRV12(244-251).
IEEE DOI
1207
BibRef
Preiswerk, F.[Frank],
Arnold, P.[Patrik],
Fasel, B.[Beat],
Cattin, P.C.[Philippe C.],
Robust tumour tracking from 2D imaging using a population-based
statistical motion model,
MMBIA12(209-214).
IEEE DOI
1203
BibRef
Zhang, W.[Wuxia],
Yuan, Y.[Yuan],
Li, X.L.[Xue-Long],
Yan, P.K.[Ping-Kun],
Learning shape statistics for hierarchical 3D medical image
segmentation,
ICIP11(2189-2192).
IEEE DOI
1201
BibRef
Lu, C.[Chao],
Duncan, J.S.[James S.],
A Non-rigid Registration Framework That Accommodates Pathology
Detection,
MLMI11(83-90).
Springer DOI
1109
BibRef
Caban, J.J.[Jesus J.],
Rheingans, P.[Penny],
Yoo, T.S.[Terry S.],
An MRF-based statistical deformation model for morphological image
analysis,
MMBIA10(31-38).
IEEE DOI
1006
BibRef
Rahim, M.,
Bellemare, M.E.,
Pirro, N.,
Bulot, R.,
A shape descriptors comparison for organs deformation sequence
characterization in MRI sequences,
ICIP09(1069-1072).
IEEE DOI
0911
BibRef
Wang, D.[Dan],
Tewfik, A.H.[Ahmed H.],
In vivo tracking of 3D organs using spherical harmonics and subspace
clustering,
ICIP09(817-820).
IEEE DOI
0911
BibRef
Ding, F.[Feng],
Yang, W.X.[Wen-Xian],
Leow, W.K.[Wee Kheng],
Venkatesh, S.K.[Sudhakar K.],
3D segmentation of soft organs by flipping-free mesh deformation,
WACV09(1-7).
IEEE DOI
0912
BibRef
Li, H.[Hao],
Leow, W.K.[Wee Kheng],
Chiu, I.S.[Ing-Sh],
Huang, S.C.[Shu-Chien],
Deformation and Smooth Joining of Mesh Models for Cardiac Surgical
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GMP08(xx-yy).
Springer DOI
0804
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Prototypes of Biological Movements in Brains and Machines,
BMCV02(157 ff.).
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0303
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Giese, M.A.[Martin Alexander],
Poggio, T.[Tomaso],
Biologically Plausible Neural Model for the Recognition of Biological
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MIT AIMAIM-2002-012, August 2002.
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Earlier:
Synthesis and Recognition of Biological Motion Patterns Based on Linear
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MVIEW99(xx-yy).
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Gee, T.F.[Timothy F.],
Wang, H.L.[Heng-Liang],
Gorbach, A.M.[Alexander M.],
Segmentation-Based Registration of Organs in Intraoperative Video
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ISVC06(II: 835-842).
Springer DOI
0611
BibRef
Liu, X.G.[Xin-Gang],
Chen, W.F.[Wu-Fan],
Elastic Registration Algorithm of Medical Images Based on Fuzzy Set,
WBIR06(214-221).
Springer DOI
0607
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Josephson, K.[Klas],
Ericsson, A.[Anders],
Karlsson, J.[Johan],
Segmentation of Medical Images Using Three-Dimensional Active Shape
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SCIA05(719-728).
Springer DOI
0506
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Pongnumkul, S.[Suchit],
Sagawa, R.[Ryusuke],
Echigo, T.[Tomio],
Yagi, Y.S.[Yasu-Shi],
Deformable Registration for Generating Dissection Image of an Intestine
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CVBIA05(271-280).
Springer DOI
0601
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Kassim, A.A.[Ashraf A.],
A New Scheme for Automatic Initialization of Deformable Models,
ICIP07(IV: 289-292).
IEEE DOI
0709
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Yan, P.K.[Ping-Kun],
Kassim, A.A.,
Medical image segmentation with minimal path deformable models,
ICIP04(IV: 2733-2736).
IEEE DOI
0505
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Lievin, M.,
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Keeve, E.,
Entropic estimation of noise for medical volume restoration,
ICPR02(III: 871-874).
IEEE DOI
0211
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Rogers, M.,
Graham, J.,
Robust Active Shape Model Search,
ECCV02(IV: 517 ff.).
Springer DOI
0205
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Marchessoux, C.,
Richard, N.,
Fernandez, C.,
Description of simple method in 3D reconstruction in medical imaging,
3DPVT02(804-807).
IEEE DOI
0206
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Automatic Generation of Shape Models Using Nonrigid Registration with a
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VMV04(73-80).
0411
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Rohlfing, T.,
Brandt, R.,
Maurer, Jr., C.R.,
Menzel, R.,
Bee Brains, B-Splines and Computational Democracy:
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MMBIA01(xx-yy).
0110
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Skrinjar, O.,
Nabavi, A.,
Duncan, J.,
A Stereo-Guided Biomechanical Model for Volumetric Deformation Analysis,
MMBIA01(xx-yy).
0110
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Tsagaan, B.,
Shimizu, A.,
Kobatake, H.,
Kunihisa, M.,
Hanzawa, Y.,
Segmentation of Kidney by Using a Deformable Model,
ICIP01(III: 1059-1062).
IEEE DOI
0108
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di Bona, S.,
Salvetti, O.,
A Deformation Model for Biotissues Behaviour Simulation,
ICIP00(Vol II: 443-446).
IEEE DOI
0008
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Zhang, C.,
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Nonlinear Distortion Correction in Endoscopic Video Images,
ICIP00(Vol II: 439-442).
IEEE DOI
0008
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Pardo, X.M.,
Radeva, P.I.,
Discriminant Snakes for 3d Reconstruction in Medical Images,
ICPR00(Vol IV: 336-339).
IEEE DOI
0009
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Wehrmann, F.,
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Thurfjell, L.,
A 3D Deformable Surface Model to Segment the Brain in MR Images,
SCIA99(Deformable Models).
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9900
Menegaz, G.,
Vaerman, V.,
Thiran, J.P.,
Object-based Coding of Volumetric Medical Data,
ICIP99(III:920-924).
IEEE DOI
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9900
Mekada, Y.[Yoshito],
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Virtualized Stomach Wall and Its Deformation Model,
ICIP99(III:412-415).
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CMU-RI-TR-99-20, October, 1999.
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Deformable Fourier Surfaces for Volume Segmentation in SPECT,
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IEEE DOI
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Segmentation of ultrasound image data by two dimensional autoregressive
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CIAP97(II: 672-679).
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9709
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Waterton, J.C.,
Quantification of Articular Cartilage from MR Images Using
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ECCV96(II:400-411).
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9600
Mangin, J.,
Regis, J.,
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Shape Bottlenecks and Conservative Flow Systems,
MMBIA96(SHAPE)
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9600
Christensen, G.,
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Synthesis of an Individualized Cranial Atlas with Dysmorphic Shape,
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0908
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Tari, S.,
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A Computationally Efficient Shape Analysis via Level Sets,
MMBIA96(DEFORMABLE MODELS II)
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9600
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A Geometric Approach to Segmentation and Analysis of 3D Medical Images,
MMBIA96(DEFORMABLE MODELS II)
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An Integrated Approach for Surface Finding in Medical Images,
MMBIA96(DEFORMABLE MODELS II)
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Applications of radar tracking algorithms to motion analysis in
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Segmentation of 3D Objects from MRI Volume Data Using
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Deformable Models for Reconstructing Unstructured 3D Data,
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Segmentation and Surface-Based Modeling of Objects in
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Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Deformable Models, Cardiac Motion Models for Volumes, Left Ventricle .