Validation of medical image processing in image-guided therapy

The Efficacy of Diagnostic Imaging

Fryback, D. G., & Thornbury, J. R. (1991) · Medical Decision Making · doi:10.1177/0272989x9101100203 · Cited by 1,294

Predicting error in rigid-body point-based registration

Fitzpatrick, J. M., West, J. B., & Maurer, C. R. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.736021 · Cited by 1,021

Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques

West, J. B., Fitzpatrick, J. M., Wang, M. Y., Dawant, B. M., Maurer, C. R., KESSLER, R. M., Maciunas, R. J., Barillot, C., Lemoine, D., Collignon, A. M. F., et al. (1997) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199707000-00007 · Cited by 883

Voxel similarity measures for 3-D serial MR brain image registration

Holden, M. P., Hill, D., Denton, E., Jarosz, J. M., Cox, T. C. S., Torsten Rohlfing, B., Goodey, J., & Hawkes, D. J. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.836369 · Cited by 275

Comparison and evaluation of retrospective intermodality image registration techniques

West, J. B., Fitzpatrick, J. M., Wang, M. Y., Dawant, B. M., Maurer, C. R., KESSLER, R. M., Maciunas, R. J., Barillot, C., Lemoine, D., Collignon, A. M. F., et al. (1996) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.237936 · Cited by 269

Voxel Similarity Measures for 3D Serial MR Brain Image Registration

Holden, M. P., Hill, D., Denton, E., Jarosz, J. M., Cox, T. C. S., & Hawkes, D. J. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48714-x_48 · Cited by 246

Design and evaluation of a system for microscope-assisted guided interventions (MAGI)

Edwards, P., King, A. P., Maurer, C. R., de Cunha, D. A., Hawkes, D. J., Hill, D., Gaston, R. P., Fenlon, M. R., Jusczyzck, A. S., Strong, A. J., et al. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.896784 · Cited by 208

Commissioning and quality assurance of an optically guided three‐dimensional ultrasound target localization system for radiotherapy

Tomé, W. A., Meeks, S. L., Orton, N. P., Bouchet, L. G., & Bova, F. J. (2002) · Medical Physics · doi:10.1118/1.1494835 · Cited by 61

Objective comparison of quantitative imaging modalities without the use of a gold standard

Hoppin, J., Kupinski, M. A., Kastis, G. A., Clarkson, E. W., & Barrett, H. H. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.1009380 · Cited by 57

Detecting Failure, Assessing Success

Fitzpatrick, J. M. (2001) · Biomedical engineering series · doi:10.1201/9781420042474.ch6 · Cited by 44

Tractography-based connectomes are dominated by false-positive connections

Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,376

A Review of 3d/2d Registration Methods for Image-Guided Interventions

Markelj, P., Tomaževič, D., Likar, B., & Pernuš, F. (2012) · Medical Image Analysis · doi:10.1016/j.media.2010.03.005 · Cited by 746

Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database

van Ginneken, B., Stegmann, M. B., & Loog, M. (2005) · Medical Image Analysis · doi:10.1016/j.media.2005.02.002 · Cited by 544

SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research

Yaniv, Z., Lowekamp, B., Johnson, H. J., & Beare, R. J. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-0037-8 · Cited by 537

Advanced Medical Displays: A Literature Review of Augmented Reality

Sielhorst, T., Marco Feuerstein, A., & Navab, N. (2008) · Journal of Display Technology · doi:10.1109/jdt.2008.2001575 · Cited by 309

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Maier‐Hein, L., Mountney, P., Bartoli, A., Elhawary, H., Elson, D. S., Groch, A., Kolb, A., Rodrigues, M., Sorger, J., Speidel, S., et al. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.04.003 · Cited by 280

Brain Functional Localization: A Survey of Image Registration Techniques

Gholipour, A., Kehtarnavaz, N., Briggs, R. W., Devous, M. D., & Gopinath, K. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.892508 · Cited by 270

Evaluation of EEG localization methods using realistic simulations of interictal spikes

Grova, C., Daunizeau, J., Lina, J.-M., Bénar, C., Benali, H., & Gotman, J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.08.053 · Cited by 59

Registration of freehand 3D ultrasound and magnetic resonance liver images

Penney, G., Blackall, J. M., Hamady, M., Sabharwal, T., Adam, A., & Hawkes, D. J. (2003) · Medical Image Analysis · doi:10.1016/j.media.2003.07.003 · Cited by 219

MRI-based measurements of respiratory motion variability and assessment of imaging strategies for radiotherapy planning

Blackall, J. M., Ahmad, S., Miquel, M. E., McClelland, J. R., Landau, D., & Hawkes, D. J. (2006) · Physics in Medicine and Biology · doi:10.1088/0031-9155/51/17/003 · Cited by 130

Simulation and Synthesis in Medical Imaging

Frangi, A. F., Tsaftaris, S. A., & Prince, J. L. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2800298 · Cited by 122

Comparative Validation of Single-Shot Optical Techniques for Laparoscopic 3-D Surface Reconstruction

Maier-Hein, L., Groch, A., Bartoli, A., Bodenstedt, S., Boissonnat, G., Chang, P.-L., Clancy, N., Elson, D. S., Haase, S., Heim, E., et al. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2325607 · Cited by 122

Statistical validation metric for accuracy assessment in medical image segmentation

Popovic, A., de la Fuente, M., Engelhardt, M., & Radermacher, K. (2007) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-007-0125-1 · Cited by 121

Semi-automatic construction of reference standards for evaluation of image registration

Murphy, K., van Ginneken, B., Klein, S., Staring, M., de Hoop, B., Viergever, M. A., & Pluim, J. P. W. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.07.005 · Cited by 111

Assessment of various strategies for 18F-FET PET-guided delineation of target volumes in high-grade glioma patients

Vees, H., Senthamizhchelvan, S., Miralbell, R., Weber, D. C., Ratib, O. M., & Zaidi, H. (2008) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-008-0943-6 · Cited by 111

An augmented reality system for liver thermal ablation: Design and evaluation on clinical cases

Nicolau, S., Pennec, X., Soler, L., Buy, X., Gangi, A., Ayache, N., & Marescaux, J. (2009) · Medical Image Analysis · doi:10.1016/j.media.2009.02.003 · Cited by 104

GIST: an interactive, GPU-based level set segmentation tool for 3D medical images

CATES, J., LEFOHN, A., & WHITAKER, R. (2004) · Medical Image Analysis · doi:10.1016/j.media.2004.06.022 · Cited by 102

Bootstrap Resampling for Image Registration Uncertainty Estimation Without Ground Truth

Kybic, J. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2030955 · Cited by 100

Alignment of sparse freehand 3-D ultrasound with preoperative images of the liver using models of respiratory motion and deformation

Blackall, J., Penney, G., King, A., & Hawkes, D. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.856751 · Cited by 107

Deformable registration for image-guided radiation therapy

Sarrut, D. (2006) · Zeitschrift für Medizinische Physik · doi:10.1078/0939-3889-00327 · Cited by 94

A protocol for evaluation of similarity measures for rigid registration

Škerl, D., Likar, B., & Pernuš, F. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.874963 · Cited by 93

Automatic Deformable MR-Ultrasound Registration for Image-Guided Neurosurgery

Rivaz, H. J., Chen, S. J.-S., & Collins, D. L. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2354352 · Cited by 83

Vessel-Based Non-Rigid Registration of MR/CT and 3D Ultrasound for Navigation in Liver Surgery

Lange, T., Eulenstein, S., Hünerbein, M., & Schlag, P. M. (2003) · Computer Aided Surgery · doi:10.3109/10929080309146058 · Cited by 83

Molecular PET/CT Imaging-Guided Radiation Therapy Treatment Planning

Zaidi, H., Vees, H., & Wissmeyer, M. (2009) · Academic Radiology · doi:10.1016/j.acra.2009.02.014 · Cited by 81

Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders

Li, W., Yang, Y., Zhang, K., Long, E., He, L., Zhang, L., Zhu, Y., Chen, C., Liu, Z., Wu, X., et al. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0577-y · Cited by 75

Case study: an evaluation of user-assisted hierarchical watershed segmentation

Cates, J. E., Whitaker, R., & Jones, G. (2005) · Medical Image Analysis · doi:10.1016/j.media.2005.04.007 · Cited by 71

New prototype neuronavigation system based on preoperative imaging and intraoperative freehand ultrasound: system description and validation

Mercier, L., Maestro, R. F. D., Petrecca, K., Kochanowska, A., Drouin, S., Yan, C. X. B., Janke, A. L., Chen, S. J.-S., & Collins, D. L. (2010) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-010-0535-3 · Cited by 67

Model for defining and reporting reference-based validation protocols in medical image processing

Jannin, P., Grova, C., & Maurer, C. R. (2006) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-006-0044-6 · Cited by 64

Augmented virtuality based on stereoscopic reconstruction in multimodal image-guided neurosurgery: methods and performance evaluation

Paul‐Gilloteaux, P., Fleig, O. J., & Jannin, P. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.857029 · Cited by 62

On mixed reality environments for minimally invasive therapy guidance: Systems architecture, successes and challenges in their implementation from laboratory to clinic

Linte, C., Davenport, K. P., Cleary, K., Peters, C., Vosburgh, K., Navab, N., Edwards, P., Jannin, P., Peters, T., Holmes, D., et al. (2013) · Comput. Medical Imaging Graph. · doi:10.1016/j.compmedimag.2012.12.002 · Cited by 60

Nonrigid registration method to assess reproducibility of breath-holding with ABC in lung cancer

Sarrut, D., Boldea, V., Ayadi, M., Badel, J., Ginestet, C., Clippe, S., & Carrié, C. (2005) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2004.08.007 · Cited by 56

Guest Editorial Validation in Medical Image Processing

Jannin, P., Krupinski, E. A., & Warfield, S. K. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.883282 · Cited by 57

Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching

Machado, I. P., Toews, M., Luo, J., Unadkat, P., Essayed, W. I., George, E., Teodoro, P., Carvalho, H., Martins, J. M., Golland, P., et al. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1786-7 · Cited by 56

Validation for 2D/3D registration I: A new gold standard data set

Pawiro, S. A., Markelj, P., Pernuš, F., Gendrin, C., Figl, M., Weber, C., Kainberger, F. M., Nöebauer-Huhmann, I.-M., Bergmeister, H., Stock, M., et al. (2011) · Medical Physics · doi:10.1118/1.3553402 · Cited by 49

Comparing two approaches to rigid registration of three-dimensional ultrasound and magnetic resonance images for neurosurgery

Mercier, L., Fonov, V., Haegelen, C., Maestro, R. F. D., Petrecca, K., & Collins, D. L. (2011) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-011-0620-2 · Cited by 49

Cortical Surface Tracking Using a Stereoscopic Operating Microscope

Sun, H., Roberts, D. W., Farid, H., Wu, Z., Hartov, A., & Paulsen, K. D. (2005) · Operative Neurosurgery · doi:10.1227/01.neu.0000146263.98583.cc · Cited by 43

Novel outcome measures for clinical trials in cystic fibrosis

Tiddens, H. A. W. M., Puderbach, M., Venegas, J. G., Ratjen, F. A., Donaldson, S. H., Davis, S. D., Rowe, S. M., Sagel, S. D., Higgins, M., & Waltz, D. A. (2014) · Pediatric Pulmonology · doi:10.1002/ppul.23146 · Cited by 42

Intensity threshold based solid tumour segmentation method for Positron Emission Tomography (PET) images: A review

Tamal, M. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e05267 · Cited by 41

Grid-Enabled Workflows for Data Intensive Medical Applications

Glatard, T., Johan Montagnat, A., & Pennec, X. (2005) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2005.61 · Cited by 41

Virtual and Augmented Medical Imaging Environments: Enabling Technology for Minimally Invasive Cardiac Interventional Guidance

Linte, C., White, J. R., Eagleson, R., Guiraudon, G., & Peters, T. (2010) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2010.2082522 · Cited by 37

Evaluation of deformable image registration and a motion model in CT images with limited features

Liu, F., Hu, Y., Zhang, Q., Kincaid, R., Goodman, K. A., & Mageras, G. S. (2012) · Physics in Medicine and Biology · doi:10.1088/0031-9155/57/9/2539 · Cited by 40

Computational Models for Image-Guided Robot-Assisted and Simulated Medical Interventions

Delingette, H., Pennec, X., Soler, L., Marescaux, J., & Ayache, N. (2006) · Proceedings of the IEEE · doi:10.1109/jproc.2006.880718 · Cited by 39

Validation for 2D/3D registration. II: the comparison of intensity- and gradient-based merit functions using a new gold standard data set

Gendrin, C., Markelj, P., Pawiro, S. A., Spoerk, J., Bloch, C., Weber, C., Figl, M., Bergmann, H., Birkfellner, W., Likar, B., et al. (2011) · Medical Physics · doi:10.1118/1.3553403 · Cited by 38

Artificial intelligence in cardiothoracic surgery

DIAS, R. D., Shah, J., & Zenati, M. A. (2020) · Minerva Cardioangiologica · doi:10.23736/s0026-4725.20.05235-4 · Cited by 38

Deformable registration of preoperative MR, pre-resection ultrasound, and post-resection ultrasound images of neurosurgery

Rivaz, H. J., & Collins, D. L. (2014) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-014-1099-4 · Cited by 34

Performance Evaluation of Grid-Enabled Registration Algorithms Using Bronze-Standards

Tristan Glatard, B., Xavier Pennec, B., & Johan Montagnat, A. (2006) · Lecture notes in computer science · doi:10.1007/11866763_19 · Cited by 35

Near Real-Time Robust Non-rigid Registration of Volumetric Ultrasound Images for Neurosurgery

Rivaz, H. J., & Collins, D. L. (2014) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2014.08.013 · Cited by 32

Modeling skeletal bone development with hidden Markov models

Giordano, D., Kavasidis, I., & Spampinato, C. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.10.012 · Cited by 32

Quantitative evaluation of image segmentation incorporating medical consideration functions

Kim, H., Monroe, J. I., Lo, S. S.-M., Yao, M., Harari, P. M., Machtay, M. X., & Sohn, J. W. (2015) · Medical Physics · doi:10.1118/1.4921067 · Cited by 36

Analyzing the co-localization of substantia nigra hyper-echogenicities and iron accumulation in Parkinson's disease: A multi-modal atlas study with transcranial ultrasound and MRI

Ahmadi, S.-A., Bötzel, K., Levin, J., Maiostre, J., Klein, T., Wein, W., Rozanski, V., Dietrich, O., Ertl-Wagner, B., Navab, N., et al. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2020.102185 · Cited by 31

ElePhant - An anatomical Electronic Phantom as simulation-system for otologic surgery

Grunert, R., Schulz, T., Oeken, J., Dietz, A., Korb, W., Strauß, G., Moeckel, H., Hofer, M., Pößneck, A., Fickweiler, U., et al. (2006) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2006.260542 · Cited by 31

Morphometric Measurements of The Retinal Vasculature in Fundus Images with Vampire

Trucco, E., Giachetti, A., Ballerini, L., Relan, D., Cavinato, A., & MacGillivray, T. (2015) · doi:10.1002/9781118715321.ch3 · Cited by 30

Registering Pre- and Postresection 3-Dimensional Ultrasound for Improved Visualization of Residual Brain Tumor

Mercier, L., Araújo, D., Haegelen, C., Maestro, R. F. D., Petrecca, K., & Collins, D. L. (2012) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2012.08.004 · Cited by 28

Risk analysis and safety assessment in surgical robotics: A case study on a biopsy robot

Korb, W., Kornfeld, M., Birkfellner, W., Boesecke, R., Figl, M., Fuerst, M. E., Kettenbach, J., Vogler, A., Haßfeld, S., & Kornreif, G. (2005) · Minimally Invasive Therapy & Allied Technologies · doi:10.1080/13645700510010827 · Cited by 28

Registration-by-regression of coronary CTA and X-ray angiography

Gouveia, A., Metz, C., Freire, L., Almeida, P., & Klein, S. (2017) · Comput. methods Biomech. Biomed. Eng. Imaging Vis. · doi:10.1080/21681163.2015.1054520 · Cited by 23

Objective and expert-independent validation of retinal image registration algorithms by a projective imaging distortion model

Lee, S.-Y., Reinhardt, J. M., Cattin, P. C., & Abràmoff, M. D. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.04.001 · Cited by 26

Registration of Pre- and Postresection Ultrasound Volumes With Noncorresponding Regions in Neurosurgery

Zhou, H., & Rivaz, H. J. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2554122 · Cited by 25

Multiple Sclerosis Medical Image Analysis and Information Management

Liu, L., Meier, D., Polgar‐Turcsanyi, M., Karkocha, P., Bakshi, R., & Guttmann, C. R. G. (2005) · Journal of Neuroimaging · doi:10.1177/1051228405282864 · Cited by 25

Advances of surgical robotics: image-guided classification and application

Li, C., Zhang, G., Zhao, B., Xie, D., Du, H., Duan, X., Hu, Y., & Zhang, L. (2024) · National Science Review · doi:10.1093/nsr/nwae186 · Cited by 28

Augmenting intraoperative ultrasound with preoperative magnetic resonance planning models for percutaneous renal access

Li, Z., Li, K., Zhan, H., Chen, K., Gu, J., & Wang, L. (2012) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-11-60 · Cited by 23

VV: a viewer for the evaluation of 4D image registration

Séroul, P., Sarrut, D., & Sarrut, D. (2008) · doi:10.54294/hfoogp · Cited by 23

Accuracy considerations in image-guided cardiac interventions: experience and lessons learned

Linte, C. A., Lang, P., Rettmann, M. E., Cho, D. S., Holmes, D. R., Robb, R. A., & Peters, T. M. (2011) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-011-0621-1 · Cited by 22

Assessment of Image-Guided Interventions

Jannin, P., & Korb, W. (2008) · Springer eBooks · doi:10.1007/978-0-387-73858-1_18 · Cited by 22

An innovative iterative thresholding algorithm for tumour segmentation and volumetric quantification on SPECT images: Monte Carlo-based methodology and validation

Pacilio, M., Basile, C., Shcherbinin, S., Caselli, F., Ventroni, G., Aragno, D., Mango, L., & Santini, E. (2011) · Medical Physics · doi:10.1118/1.3590359 · Cited by 21

Statistical Computing on Manifolds for Computational Anatomy

Pennec, X. (2006) · Cited by 21

Accuracy of needle implantation in brachytherapy using a medical AR system: a phantom study

Wesarg, S., Firle, E. A., Schwald, B., Seibert, H., Żogał, P., & Roddiger, S. J. (2004) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.535415 · Cited by 21

Image registration and mosaicing for dynamic In vivo fibered confocal microscopyPreprint

Vercauteren, T. K. (2008) · Cited by 19

How accurate is accurate enough? A brief overview on accuracy considerations in image-guided cardiac interventions

Linte, C. A., Moore, J. T., & Peters, T. M. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5627652 · Cited by 17

Automatic extraction of proximal femur contours from calibrated X‐ray images using 3D statistical models: anin vitrostudy

Dong, X. M., & Guoyan Zheng, A. (2009) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.253 · Cited by 17

Augmented-reality-driven medical simulation platform for percutaneous nephrolithotomy with cybersecurity awareness

Tai, Y., Wei, L., Zhou, H., Peng, J., Li, Q., Li, F., Zhang, J., & Shi, J. (2019) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147719840173 · Cited by 17

Comparative evaluation of similarity measures for the rigid registration of multi-modal head images

Škerl, D., Likar, B., Fitzpatrick, J. M., & Pernuš, F. (2007) · Physics in Medicine and Biology · doi:10.1088/0031-9155/52/18/008 · Cited by 17

Comparative survey of multigraph integration methods for holistic brain connectivity mapping

Chaari, N., Akdağ, H. C., & Rekik, I. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102741 · Cited by 18

Learning anatomy changes from patient populations to create artificial CT images for voxel‐level validation of deformable image registration

Yu, Z., Kudchadker, R. J., Dong, L., Zhang, Y., Court, L. E., Mourtada, F., Yock, A. D., Tucker, S. L., & Yang, J. (2016) · Journal of Applied Clinical Medical Physics · doi:10.1120/jacmp.v17i1.5888 · Cited by 16

Evaluating soft tissue simulation in maxillofacial surgery using preoperative and postoperative CT scans

Chabanas, M., Marécaux, C., Chouly, F., Boutault, F., & Yohan Payan, A. (2004) · International Congress Series · doi:10.1016/j.ics.2004.03.165 · Cited by 15

Computer Vision Techniques for Transcatheter Intervention

Zhao, F., Xie, X., & Roach, M. (2015) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2015.2446988 · Cited by 14

MatchPoint: On Bridging the Innovation Gap between Algorithmic Research and Clinical Use in Image Registration

Floca, R. O. (2009) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-642-03882-2_294 · Cited by 14

A flexible registration and evaluation engine (f.r.e.e.)

Floca, R., & Dickhaus, H. (2007) · Comput. Methods Programs Biomed. · doi:10.1016/j.cmpb.2007.04.009 · Cited by 19

Rigid Registration of 3D Ultrasound and MRI: Comparing Two Approaches on Nine Tumor Cases

Mercier, L., Fonov, V., Maestro, R. F. D., Petrecca, K., Østergaard, L. R., & Collins, D. L. (2010) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-16259-6_3 · Cited by 13

Augmented reality-based surgical navigation of pelvic screw placement: an ex-vivo experimental feasibility study

Heining, S.-M., Raykov, V., Wolff, O., Alkadhi, H., Pape, H., & Wanner, G. A. (2024) · Patient Safety in Surgery · doi:10.1186/s13037-023-00385-6 · Cited by 13

A framework for automatic creation of gold-standard rigid 3D–2D registration datasets

Madan, H., Pernuš, F., Likar, B., & Špiclin, Ž. (2016) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1482-4 · Cited by 13

A Review of 3D-2D Registration Methods and Applications based on Medical Images

Sun, H. (2023) · Highlights in Science Engineering and Technology · doi:10.54097/hset.v35i.7055 · Cited by 12

A 4D biomechanical lung phantom for joint segmentation/registration evaluation

Markel, D., Levesque, I. R., Larkin, J., Léger, P., & Naqa, I. E. (2016) · Physics in Medicine and Biology · doi:10.1088/0031-9155/61/19/7012 · Cited by 12

Assessment of Rigid Registration Quality Measures in Ultrasound-Guided Radiotherapy

Shams, R., Xiao, Y., Hebert, F., Abramowitz, M. C., Brooks, R., & Rivaz, H. J. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2755695 · Cited by 12

Two‐dimensional ultrasound‐computed tomography image registration for monitoring percutaneous hepatic intervention

Pohlman, R. M., Turney, M. R., Wu, P., Brace, C. L., Ziemlewicz, T. J., & Varghese, T. (2019) · Medical Physics · doi:10.1002/mp.13554 · Cited by 11

Intensity-based registration of freehand 3D ultrasound and CT-scan images of the kidney

Leroy, A., Mozer, P., Yohan Payan, A., & Troccaz, J. (2007) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-007-0077-5 · Cited by 11

Imaging Services on the Grid as a Product Line: Requirements and Architecture

Acher, M., Collet, P., Johan Montagnat, A., Hal, H. I., & Acher, M. (2008) · Cited by 11

On the modeling, construction, and evaluation of a probabilistic atlas of brain perfusion

Vik, T., Heitz, F., Namer, I. J., & Armspach, J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.10.019 · Cited by 11

Three‐dimensional quantitative evaluation method of nonrigid registration algorithms for adaptive radiotherapy

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Image Registration Accuracy Estimation Without Ground Truth Using Bootstrap

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Author manuscript, published in "IEEE Transactions on Medical Imaging 2002;21(12):1445-9"> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE DOI: 10.1109/TMI.2002.806568 TO EDIT) < 1 Validation of Medical Image Processing in Image

Jannin, P., Fitzpatrick, J. M., Hawkes, D. J., Pennec, X., Shahidi, R., & Michael, W. (2013)

White paper: validation of medical image processing in image-guided therapy

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c Computational Imaging Science Group, London GB

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Challenges in image-guided therapy system design.

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