21.1.3 Anotomical Landmark Detection, Landmark Location in Various Sensors

Chapter Contents (Back)
Landmark Detection. Landmark Location.

Romaniuk, B., Desvignes, M., Revenu, M., Deshayes, M.J.,
Shape variability and spatial relationships modeling in statistical pattern recognition,
PRL(25), No. 2, January 2004, pp. 239-247.
Elsevier DOI 0401
BibRef
And:
Contour tracking by minimal cost path approach. application to cephalometry,
ICIP04(II: 937-940).
IEEE DOI 0505
BibRef
Earlier:
Linear and non-linear model for statistical localization of landmarks,
ICPR02(IV: 393-396).
IEEE DOI 0211
BibRef

Romaniuk, B., Desvignes, M., Robiaille, J., Revenu, M., Deshayes, M.J.,
Augmented Reality and Semi-automated Landmarking of Cephalometric Radiographs,
CAIP01(410 ff.).
Springer DOI 0210
BibRef

Desvignes, M., Romaniuk, B., Clouard, R., Demoment, R., Revenu, M., Deshayes, M.J.,
First Steps Toward Automatic Location of Landmarks on X-ray Images,
ICPR00(Vol II: 275-278).
IEEE DOI 0009
BibRef

El-Feghi, I., Sid-Ahmed, M.A., Ahmadi, M.,
Automatic localization of craniofacial landmarks for assisted cephalometry,
PR(37), No. 3, March 2004, pp. 609-621.
Elsevier DOI 0401
BibRef

Liu, X.[Xiang], Kim, W.[Wangdo], Drerup, B.[Burkhard],
3D characterization and localization of anatomical landmarks of the foot by FastSCAN,
RealTimeImg(10), No. 4, August 2004, pp. 217-228.
Elsevier DOI 0410
BibRef

El-Feghi, I., Sid-Ahmed, M.A., Ahmadi, M.,
Automatic localization of craniofacial landmarks using multi-layer perceptron as a function approximator,
PRL(27), No. 6, 15 April 2006, pp. 544-550.
Elsevier DOI Cephalometric; Cephalometric evaluation; X-ray image; MLP; Craniofacial 0604
BibRef

Iglesias, J.E., Karssemeijer, N.,
Robust Initial Detection of Landmarks in Film-Screen Mammograms Using Multiple FFDM Atlases,
MedImg(28), No. 11, November 2009, pp. 1815-1824.
IEEE DOI 0911
BibRef

Ibragimov, B., Likar, B., Pernus, F., Vrtovec, T.,
A Game-Theoretic Framework for Landmark-Based Image Segmentation,
MedImg(31), No. 9, September 2012, pp. 1761-1776.
IEEE DOI 1209
BibRef

Ibragimov, B., Likar, B., Pernus, F., Vrtovec, T.,
Shape Representation for Efficient Landmark-Based Segmentation in 3-D,
MedImg(33), No. 4, April 2014, pp. 861-874.
IEEE DOI 1404
Image segmentation BibRef

Potesil, V.[Vaclav], Kadir, T.[Timor], Sir Brady, M.[Michael],
Learning New Parts for Landmark Localization in Whole-Body CT Scans,
MedImg(33), No. 4, April 2014, pp. 836-848.
IEEE DOI 1404
Adaptation models
See also Personalized Graphical Models for Anatomical Landmark Localization in Whole-Body Medical Images. BibRef

Kaur, A.[Amandeep], Singh, C.[Chandan],
Automatic cephalometric landmark detection using Zernike moments and template matching,
SIViP(9), No. 1, January 2015, pp. 117-132.
WWW Link. 1503
BibRef

Wang, C.W., Huang, C.T., Hsieh, M.C., Li, C.H., Chang, S.W., Li, W.C., Vandaele, R., Maree, R., Jodogne, S., Geurts, P., Chen, C., Zheng, G., Chu, C., Mirzaalian, H., Hamarneh, G., Vrtovec, T., Ibragimov, B.,
Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge,
MedImg(34), No. 9, September 2015, pp. 1890-1900.
IEEE DOI 1509
Biomedical imaging 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

Ibragimov, B., Korez, R., Likar, B., Pernuš, F., Xing, L., Vrtovec, T.,
Segmentation of Pathological Structures by Landmark-Assisted Deformable Models,
MedImg(36), No. 7, July 2017, pp. 1457-1469.
IEEE DOI 1707
Computational modeling, Deformable models, Image edge detection, Image segmentation, Laplace equations, Pathology, Shape, Laplacian mesh editing, corpus callosum segmentation, deformablemodels, landmark detection, pathology analysis, prostate segmentation, vertebra, segmentation BibRef

Norajitra, T.[Tobias], Maier-Hein, K.H.[Klaus H.],
3D Statistical Shape Models Incorporating Landmark-Wise Random Regression Forests for Omni-Directional Landmark Detection,
MedImg(36), No. 1, January 2017, pp. 155-168.
IEEE DOI 1701
Adaptation models BibRef

Jacinto, H.[Hector], Valette, S.[Sébastien], Prost, R.[Rémy],
Multi-atlas automatic positioning of anatomical landmarks,
JVCIR(50), 2018, pp. 167-177.
Elsevier DOI 1802
Orthopaedics, Knee, Landmarks, Atlas, Registration, Positioning BibRef

Jacinto, H.[Hector], Kechichian, R., Valette, S.[Sébastien], Prost, R.[Rémy],
Positioning of anatomical landmarks in orthopedics by MESH registration,
ICIP14(3572-3576)
IEEE DOI 1502
Biomedical imaging BibRef

Maghsoudi, O.H.[Omid Haji], Vahedipour, A.[Annie], Spence, A.[Andrew],
Three-dimensional-based landmark tracker employing a superpixels method for neuroscience, biomechanics, and biology studies,
IJIST(29), No. 4, 2019, pp. 419-430.
DOI Link 1911
3D reconstruction, animal tracking, biomechanics, Kalman filter, neuroscience, spinal cord injury, superpixels BibRef

Torosdagli, N., Liberton, D.K., Verma, P., Sincan, M., Lee, J.S., Bagci, U.,
Deep Geodesic Learning for Segmentation and Anatomical Landmarking,
MedImg(38), No. 4, April 2019, pp. 919-931.
IEEE DOI 1904
Image segmentation, Bones, Computed tomography, Computer architecture, Image analysis, cone beam computed tomography (CBCT) 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

Chen, R.N.[Run-Nan], Ma, Y.X.[Yue-Xin], Chen, N.L.[Neng-Lun], Liu, L.J.[Ling-Jie], Cui, Z.M.[Zhi-Ming], Lin, Y.H.[Yan-Hong], Wang, W.P.[Wen-Ping],
Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection,
MedImg(41), No. 7, July 2022, pp. 1791-1801.
IEEE DOI 2207
Heating systems, Nose, Learning systems, Knowledge based systems, Computed tomography, Bones, CBCT, anatomical landmark detection, LSTM BibRef

Schobs, L.A.[Lawrence Andrew], Swift, A.J.[Andrew J.], Lu, H.P.[Hai-Ping],
Uncertainty Estimation for Heatmap-Based Landmark Localization,
MedImg(42), No. 4, April 2023, pp. 1021-1034.
IEEE DOI 2304
Uncertainty, Heating systems, Location awareness, Measurement uncertainty, Task analysis, Image segmentation, U-Net BibRef

Guo, Y.Y.[Yu-Yu], Bi, L.[Lei], Wei, D.M.[Dong-Ming], Chen, L.Y.[Li-Yun], Zhu, Z.B.[Zheng-Bin], Feng, D.[Dagan], Zhang, R.Y.[Rui-Yan], Wang, Q.[Qian], Kim, J.M.[Jin-Man],
Unsupervised Landmark Detection-Based Spatiotemporal Motion Estimation for 4-D Dynamic Medical Images,
Cyber(53), No. 6, June 2023, pp. 3532-3545.
IEEE DOI 2305
Strain, Motion estimation, Biomedical imaging, Dynamics, Image registration, Topology, Optimization, Image registration, unsupervised landmark detection BibRef

Huang, Z.X.[Zi-Xun], Zhao, R.[Rui], Leung, F.H.F.[Frank H. F.], Banerjee, S.[Sunetra], Lam, K.M.[Kin-Man], Zheng, Y.P.[Yong-Ping], Ling, S.H.[Sai Ho],
Landmark Localization From Medical Images With Generative Distribution Prior,
MedImg(43), No. 7, July 2024, pp. 2679-2692.
IEEE DOI Code:
WWW Link. 2407
Heating systems, Location awareness, Biomedical imaging, Task analysis, Training, Estimation, Detectors, heatmap-based localization BibRef


Song, Y.[Yu], Qiao, X.[Xu], Iwamoto, Y.[Yutaro], Chen, Y.W.[Yen-Wei],
A Teacher-Student Learning Based on Composed Ground-Truth Images for Accurate Cephalometric Landmark Detection,
ICIP21(3777-3781)
IEEE DOI 2201
Location awareness, Backpropagation, Image processing, MIMICs, Predictive models, Convolutional neural networks, ground-truth BibRef

Palazzo, S., Bellitto, G., Prezzavento, L., Rundo, F., Bagci, U., Giordano, D., Leonardi, R., Spampinato, C.,
Deep Multi-stage Model for Automated Landmarking of Craniomaxillofacial CT Scans,
ICPR21(9982-9987)
IEEE DOI 2105
Image segmentation, Image resolution, Computed tomography, Atmospheric modeling, Pipelines, Anatomical structure, Aerospace electronics BibRef

Quan, Q.[Quan], Yao, Q.S.[Qing-Song], Li, J.[Jun], Zhou, S.K.[S. Kevin],
Which images to label for few-shot medical landmark detection?,
CVPR22(20574-20584)
IEEE DOI 2210
Training, Deep learning, Annotations, Training data, Estimation, Feature extraction, Medical, biological and cell microscopy, Self- semi- meta- Transfer/low-shot/long-tail learning BibRef

Yin, Z.[Zihao], Gong, P.[Ping], Wang, C.Y.[Chun-Yu], Yu, Y.Z.[Yi-Zhou], Wang, Y.Z.[Yi-Zhou],
One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement,
ECCV22(XXI:473-489).
Springer DOI 2211
BibRef

O'Neil, A.Q.[Alison Q.], Kascenas, A.[Antanas], Henry, J.[Joseph], Wyeth, D.[Daniel], Shepherd, M.[Matthew], Beveridge, E.[Erin], Clunie, L.[Lauren], Sansom, C.[Carrie], Šeduikyte, E.[Evelina], Muir, K.[Keith], Poole, I.[Ian],
Attaining Human-Level Performance with Atlas Location Autocontext for Anatomical Landmark Detection in 3D CT Data,
DeepLearn-G18(III:470-484).
Springer DOI 1905
BibRef

Riegler, G., Urschler, M.[Martin], Ruther, M., Bischof, H.[Horst], Stern, D.,
Anatomical Landmark Detection in Medical Applications Driven by Synthetic Data,
TASKCV15(85-89)
IEEE DOI 1602
Biomedical imaging BibRef

Bieth, M.[Marie], Donner, R.[Rene], Langs, G.[Georg], Schwaiger, M.[Markus], Menze, B.H.[Bjoern H.],
Anatomical triangulation: from sparse landmarks to dense annotation of the skeleton in CT images,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Gass, T.[Tobias], Szekely, G.[Gabor], Goksel, O.[Orcun],
Multi-atlas Segmentation and Landmark Localization in Images with Large Field of View,
MCV14(171-180).
Springer DOI 1501
BibRef

Masutani, Y.[Yoshitaka], Nemoto, M.[Mitsutaka], Hanaoka, S.[Shohei], Hayashi, N.[Naoto], Ohtomo, K.[Kuni],
Appearance Similarity Flow for Quantification of Anatomical Landmark Uncertainty in Medical Images,
ISVC12(I: 12-21).
Springer DOI 1209
BibRef

Demir, A.[Ali], Unal, G.[Gozde], Karaman, K.[Kutlay],
Anatomical Landmark Based Registration of Contrast Enhanced T1-Weighted MR Images,
WBIR10(91-103).
Springer DOI 1007
BibRef

Ma, L.H.[Li-Hong], Jiang, S.M.[Sheng-Min], Zhang, Y.[Yu], Lin, C.Y.[Chun-Yi], Lu, H.Q.[Han-Qing],
Craniofacial Landmark Detection by Layered Diffusion and Dilated Skeleton Maps,
ICARCV06(1-5).
IEEE DOI 0612
BibRef

Burnsides, D., Boehmer, M., Robinette, K.M.,
3-D landmark detection and identification in the CAESAR project,
3DIM01(393-398).
IEEE DOI 0106
BibRef

Robinette, K.M., Daanen, H.A.M., Paquet, E.,
The CAESAR project: a 3-D surface anthropometry survey,
3DIM99(380-386).
IEEE DOI 9910
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications, Microscope Image Analysis .


Last update:Nov 26, 2024 at 16:40:19