Comparison and evaluation of methods for liver segmentation from CT datasets
Abstract
This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In many medical image analysis problems, localizing anatomical structures is a prerequisite.
“In many medical image analysis problems, localization anatomical structures is a prerequisite.”
From Self-supervised learning for medical image analysis using image context restoration (2019) · cites this paper as Heimann et al., 2009
Cites
(1967) · Cited by 23,719 · see citing works
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · see citing works
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466 · see citing works
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936 · see citing works
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588 · see citing works
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.015 · Cited by 8,868 · see citing works
Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in industrial control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,938 · see citing works
Nonrigid registration using free-form deformations: application to breast mr images
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.796284 · Cited by 5,265 · see citing works
Multimodality image registration by maximization of mutual information
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.563664 · Cited by 4,547 · see citing works
Comparing images using the Hausdorff distance
Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.232073 · Cited by 4,545 · see citing works
Level Set Methods and Fast Marching Methods
Sethian, J. A. (1999) · Medical Entomology and Zoology · Cited by 3,588 · see citing works
Shape modeling with front propagation: a level set approach
Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.368173 · Cited by 3,166 · see citing works
Artificial Intelligence through Simulated Evolution
Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966) · International Linear Collider · Cited by 2,814 · see citing works
Pattern Classification (2nd ed.)
Duda, R. O., Hart, P. E., & Stork, D. G. (1999) · Cited by 2,509 · see citing works
Étude comparative de la distribution florale dans une portion des Alpes et du Jura
Jaccard, P. (1901) · E-Periodica · doi:10.5169/seals-266450 · Cited by 2,409 · see citing works
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R., & Wu, A. Y. (1998) · Journal of the ACM · doi:10.1145/293347.293348 · Cited by 2,290 · see citing works
Schwefel, H. (1995) · Cited by 2,197 · see citing works
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072 · see citing works
Graph Cuts and Efficient N-D Image Segmentation
Boykov, Y., & Funka-Lea, G. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7934-5 · Cited by 1,910 · see citing works
Reconstruction and representation of 3D objects with radial basis functions
Carr, J. C., Beatson, R. K., Cherrie, J. B., Mitchell, T. J., Fright, W. R., McCallum, B. C., & Evans, T. R. (2001) · Proceedings of the 28th annual conference on Computer graphics and interactive techniques · doi:10.1145/383259.383266 · Cited by 1,852 · see citing works
Image processing, analysis and machine vision
Dijkstra, T. (1994) · Neurocomputing · doi:10.1016/0925-2312(94)90073-6 · Cited by 1,818 · see citing works
The EM Algorithm and Extensions, 2E
McLachlan, G. J., & Krishnan, T. (2008) · John Wiley & Sons, Inc. eBooks · doi:10.1002/9780470191613 · Cited by 1,536 · see citing works
Deformable templates using large deformation kinematics
Christensen, G. E., Rabbitt, R. D., & Miller, M. I. (1996) · IEEE Transactions on Image Processing · doi:10.1109/83.536892 · Cited by 1,146 · see citing works
Efficient and reliable schemes for nonlinear diffusion filtering
Weickert, J., Romeny, B. M. T. H., & Viergever, M. A. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.661190 · Cited by 1,137 · see citing works
A survey on evaluation methods for image segmentation
Zhang, Y. J. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00169-7 · Cited by 1,060 · see citing works
Use of active shape models for locating structures in medical images
Cootes, T. F., Hill, A., Taylor, C., et al. (1994) · Image and Vision Computing · doi:10.1016/0262-8856(94)90060-4 · Cited by 912 · see citing works
A shape-based approach to the segmentation of medical imagery using level sets
Tsai, A., Yezzi, A. J., Wells, W. M., Tempany, C. M., Tucker, D., Fan, A., Grimson, W. E. L., & Willsky, A. S. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.808355 · Cited by 895 · see citing works
Surface Parameterization: a Tutorial and Survey
Floater, M. S., & Hormann, K. (2005) · Springer eBooks · doi:10.1007/3-540-26808-1_9 · Cited by 883 · see citing works
PET-CT image registration in the chest using free-form deformations
Mattes, D., Haynor, D. R., Vesselle, H. J., Lewellen, T. K., & Eubank, W. B. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.809072 · Cited by 867 · see citing works
Multi-modality image registration by maximization of mutual information
Maes, F., Collignon, A. M. F., Vandermeulen, D., Marchal, G., & Suetens, P. (1996) · IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) · doi:10.1109/mmbia.1996.534053 · Cited by 809 · see citing works
Geometric Level Set Methods in Imaging, Vision, and Graphics
Osher, S., & Paragios, N. (2003) · Springer eBooks · doi:10.1007/b97541 · Cited by 802 · see citing works
MRI segmentation: Methods and applications
Clarke, L. P., Velthuizen, R. P., Camacho, M. A., Heine, J., Vaidyanathan, M., Hall, L., Thatcher, R. W., & Silbiger, M. L. (1995) · Magnetic Resonance Imaging · doi:10.1016/0730-725x(94)00124-l · Cited by 697 · see citing works
An optimal algorithm for approximate nearest neighbor searching
Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R., & Wu, A. Y. (1994) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · doi:10.5555/314464.314652 · Cited by 590 · see citing works
Lung Image Database Consortium: Developing a Resource for the Medical Imaging Research Community
Armato, S. G., McLennan, G., McNitt‐Gray, M. F., Meyer, C. R., Yankelevitz, D. F., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., et al. (2004) · Radiology · doi:10.1148/radiol.2323032035 · Cited by 381 · see citing works
Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation
Gerig, G., Jomier, M., & Chakos, M. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45468-3_62 · Cited by 345 · see citing works
Construction of an abdominal probabilistic atlas and its application in segmentation
Park, H., Bland, P. H., & Meyer, C. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.809139 · Cited by 330 · see citing works
Soler, L., Delingette, H., Malandain, G., Johan Montagnat, A., Ayache, N., Koehl, C., Dourthe, O., Malassagne, B., Smith, M., Mutter, D., et al. (2001) · Computer Aided Surgery · doi:10.1002/igs.1016 · Cited by 322 · see citing works
Segmentation of medical images using adaptive region growing
Pohle-Fröhlich, R., & Toennies, K. D. (2001) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.431013 · Cited by 315 · see citing works
An automatic diagnostic system for CT liver image classification
Chen, E.-L., Chung, P., Chen, C.-L., et al. (1998) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.678613 · Cited by 298 · see citing works
Gletsos, M., Mougiakakou, S. G., Matsopoulos, G. K., Nikita, K. S., Nikita, A. S., & Kelekis, A. (2003) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2003.813793 · Cited by 248 · see citing works
Soler, L., Delingette, H., Malandain, G., et al. (2001) · Computer Aided Surgery · doi:10.3109/10929080109145999 · Cited by 239 · see citing works
Quo Vadis, Atlas-Based Segmentation?
Torsten Rohlfing, B., Brandt, R., Menzel, R., Russakoff, D. B., & Maurer, C. R. (2005) · Springer eBooks · doi:10.1007/0-306-48608-3_11 · Cited by 188 · see citing works
Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model
Kainmüller, D., Lange, T., & Lamecker, H. (2007) · Cited by 173 · see citing works
Self-citationEfficient Kernel Density Estimation of Shape and Intensity Priors for Level Set Segmentation
Rousson, M., & Cremers, D. (2005) · Lecture notes in computer science · doi:10.1007/11566489_93 · Cited by 140 · see citing works
Self-citationVariable-conductance, level-set curvature for image denoising
Whitaker, R., & Xue, X. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2001.958071 · Cited by 130 · see citing works
Segmentation of multiple organs in non-contrast 3D abdominal CT images
Shimizu, A., Ohno, R., Ikegami, T., et al. (2007) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-007-0135-z · Cited by 125 · see citing works
Self-citationSegmentation of the Liver using a 3D Statistical Shape Model
Lamecker, H., Lange, T., & Seebaß, M. (2004) · Cited by 112 · see citing works
Self-citationOn evaluating brain tissue classifiers without a ground truth
Sylvain Bouix, A., Martín‐Fernández, M., Ungar, L., Nakamura, M., Koo, M., McCarley, R. W., & Shenton, M. E. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.031 · Cited by 98 · see citing works
Radtke, A., Nadalin, S., Sotiropoulos, G. C., Molmenti, E. P., Schroeder, T., Valentín-Gamazo, C., Lang, H., Bockhorn, M., Peitgen, H. O., Broelsch, C. E., et al. (2006) · World Journal of Surgery · doi:10.1007/s00268-005-0718-1 · Cited by 94 · see citing works
Fully automatic liver segmentation for contrast-enhanced CT images
Ruskó, L., Bekes, G., Németh, G., et al. (2008) · Cited by 94 · see citing works
Self-citationComputerized planning of liver surgery—an overview
Meinzer, H., Thorn, M., & Cardenas, C. E. (2002) · Computers & Graphics · doi:10.1016/s0097-8493(02)00102-4 · Cited by 90 · see citing works
Self-citationAutomatic 3D segmentation of the liver from abdominal CT images: a level-set approach
Pan, S., & Dawant, B. M. (2001) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.431019 · Cited by 80 · see citing works
Self-citationA Statistical Deformable Model for the Segmentation of Liver CT Volumes
Heimann, T., Meinzer, H., & Wolf, I. (2007) · Cited by 69 · see citing works
Self-citationSimultaneous Segmentation of Multiple Closed Surfaces Using Optimal Graph Searching
Li, K., Millington, S. A., Wu, X., et al. (2005) · Lecture notes in computer science · doi:10.1007/11505730_34 · Cited by 55 · see citing works
Multisite validation of image analysis methods: assessing intra- and intersite variability
Styner, M. A., Charles, H. C., Park, J. K., & Gerig, G. (2002) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.467167 · Cited by 55 · see citing works
Self-citationvan Rikxoort, E. M., Arzhaeva, Y., & van Ginneken, B. (2007) · Cited by 55 · see citing works
Self-citationLiver Segmentation in CT Data: A Segmentation Refinement Approach
Beichel, R., Bauer, C. H., Bornik, A., et al. (2007) · Cited by 50 · see citing works
Self-citationDawant, B. M., Li, R., Lennon, B., & Li, S. (2007) · Cited by 50 · see citing works
Self-citationA Hybrid User Interface for Manipulation of Volumetric Medical Data
Bornik, A., Beichel, R., Kruijff, E., et al. (2006) · doi:10.1109/vr.2006.8 · Cited by 45 · see citing works
Self-citationFurukawa, D., Shimizu, A., & Kobatake, H. (2007) · Cited by 43 · see citing works
Self-citationLandmark based liver segmentation using local shape and local intensity models
Seghers, D., Slagmolen, P., Lambelin, Y., et al. (2007) · Medical Image Computing and Computer-Assisted Intervention · Cited by 42 · see citing works
Self-citationTwo-stage Semi-automatic Organ Segmentation Framework using Radial Basis Functions and Level Sets
Wimmer, A., Soza, G., & Joachim Hornegger, A. (2007) · Cited by 42 · see citing works
Self-citationGlobal-to-Local Shape Matching for Liver Segmentation in CT Imaging
Saddi, K. A., Chefd, C., & Farida Cheriet, A. (2007) · Cited by 39 · see citing works
Self-citationAnything you can do, I can do better (No you can't)…
Price, K. (1986) · Computer Vision Graphics and Image Processing · doi:10.1016/0734-189x(86)90083-6 · Cited by 37 · see citing works
HepaTux – A Semiautomatic Liver Segmentation System
Beck, A., & Aurich, V. (2010) · Cited by 37 · see citing works
Self-citationEcient Liver Segmentation exploiting Level-Set Speed Images with 2.5D Shape Propagation
Lee, J., Kim, N., Lee, K. H., et al. (2007) · Cited by 32 · see citing works
Self-citationChi, Y., Cashman, P. M., Bello, F., & Kitney, R. I. (2007) · Cited by 30 · see citing works
Self-citationAtlas based liver segmentation using nonrigid registration with a B-spline transformation model
Slagmolen, P., Elen, A., Seghers, D., Loeckx, D., Maes, F., & Haustermans, K. M. (2007) · Cited by 30 · see citing works
Self-citationCognition Network Technology for a Fully Automated 3D Segmentation of Liver
Schmidt, G., Athelogou, M., Korn, R., et al. (2007) · Cited by 30 · see citing works
Self-citationNew methods for leak detection and contour correction in seeded region growing segmentation
Heimann, T., Thor�n, M., Kunert, T., et al. (2004) · Cited by 25 · see citing works
Self-citationAlkoot, F. M., & Kittler, J. (2002) · Pattern Analysis and Applications · doi:10.1007/s100440200029 · Cited by 25 · see citing works
Automatic Generation of 3D Statistical Shape Models with Optimal Landmark Distributions
Wolf, I., Meinzer, H.-P., & Heimann, T. (2007) · Methods of Information in Medicine · doi:10.1160/me9043 · Cited by 22 · see citing works
Self-citationError Metrics for Quantitative Evaluation of Medical Image Segmentation
Niessen, W. J., Bouma, C. J., Vincken, K. L., et al. (2000) · Computational imaging and vision · doi:10.1007/978-94-015-9538-4_22 · Cited by 21 · see citing works
Automated detection of pulmonary nodules in helical computed tomography images of the thorax
Armato, S. G., Giger, M. L., Moran, C. J., et al. (1998) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.310968 · Cited by 13 · see citing works
Shimizu, A., & Nawano, S. (2004) · International Congress Series · doi:10.1016/j.ics.2004.03.312 · Cited by 10 · see citing works
Self-citationInteractive editing of segmented volumetric datasets in a hybrid 2D/3D virtual environment
Bornik, A., Beichel, R., & Schmalstieg, D. (2006) · ACM Symposium on Virtual Reality Software and Technology (VRST) · doi:10.1145/1180495.1180536 · Cited by 10 · see citing works
Self-citationMethods for image segmentation should be standardized and calibrated
Chaney, E. L., Ibbott, G. S., & Hendee, W. R. (2005) · Medical Physics · doi:10.1118/1.2131093 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · see citing works
The Liver Tumor Segmentation Benchmark (LiTS)
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102680 · Cited by 1,412 · see citing works
Evaluation of prostate segmentation algorithms for mri: The promise12 challenge
Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2013.12.002 · Cited by 857 · see citing works
Christ, P. F., Elshaer, M. E. A., Ettlinger, F., Tatavarty, S., Bickel, M., Bilic, P., Rempfler, M., Armbruster, M., Hofmann, F., D’Anastasi, M., et al. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_48 · Cited by 625 · see citing works
Self-supervised learning for medical image analysis using image context restoration
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101539 · Cited by 575 · see citing works
Segment anything model for medical images?
Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103061 · Cited by 536 · see citing works
“The authors of this paper sincerely appreciate all the challenge organizers and owners for providing the public MIS datasets including AbdomenCT-1K, ACDC, AMOS 2022, AutoLaparo,BrainPTM 2021, BraTS20,CAMUS, CHAOS, CHASE-DB1, Chest CT segmentation, CRAG, crossMoDA, CVC-ClinicDB,DRIVE, EndoTect 2020, EPFL-EM, ETIS-Larib…”
RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans
Jin, Q., Meng, Z., Sun, C., Cui, H., & Su, R. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.605132 · Cited by 468 · see citing works
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Dou, Q., Chen, H., Jin, Y., Yu, L., Qin, J., & Heng, P.-A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_18 · Cited by 344 · see citing works
Computer-aided Diagnosis: How to Move from the Laboratory to the Clinic
van Ginneken, B., Schaefer-Prokop, C. M., & Prokop, M. (2011) · Radiology · doi:10.1148/radiol.11091710 · Cited by 306 · see citing works
Self-citationAutomatic 3D liver location and segmentation via convolutional neural network and graph cut
Lu, F., Wu, F., Hu, P., Peng, Z., & Kong, D. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1467-3 · Cited by 284 · see citing works
Medical image segmentation on GPUs – A comprehensive review
Smistad, E., Falch, T. L., Bozorgi, M., Elster, A. C., & Lindseth, F. (2015) · Medical Image Analysis · doi:10.1016/j.media.2014.10.012 · Cited by 264 · see citing works
Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images
Li, G., Chen, X., Shi, F., Zhu, W., Tian, J., & Xiang, D. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2481326 · Cited by 185 · see citing works
A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes
Li, C., Wang, X., Eberl, S., Fulham, M., Yin, Y., Chen, J., & Feng, D. D. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2267212 · Cited by 139 · see citing works
Federated learning for healthcare applications
Chaddad, A., Wu, Y., & Desrosiers, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3325822 · Cited by 136 · see citing works
Zhang, L., Ye, X., Lambrou, T., Duan, W., Allinson, N., & Dudley, N. J. (2016) · Physics in Medicine & Biology · doi:10.1088/0031-9155/61/3/1095 · Cited by 34 · see citing works
Feature-based fuzzy connectedness segmentation of ultrasound images with an object completion step
Rueda, S., Knight, C. L., Papageorghiou, A. T., & Alison Noble, J. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.07.002 · Cited by 30 · see citing works
SEMI-automated liver CT segmentation using Laplacian meshes
Chartrand, G., Cresson, T., Chav, R., Gotra, A., Tang, A., & DeGuise, J. (2014) · 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6867952 · Cited by 32 · see citing works
- Authors
- Heimann, T., van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., Bello, F., Binnig, G., Bischof, H., Bornik, A., Cashman, P., Chi, Y., Cordova, A., Dawant, B. M., Fidrich, M., Furst, J. D., Furukawa, D., Grenacher, L., Hornegger, J., Kainmuller, D., Kitney, R. I., Kobatake, H., Lamecker, H., Lange, T., Lee, J., Lennon, B., Li, R., Li, S., Meinzer, H.-P., Nemeth, G., Raicu, D. S., Rau, A.-M., van Rikxoort, E. M., Rousson, M., Rusko, L., Saddi, K. A., Schmidt, G., Seghers, D., Shimizu, A., Slagmolen, P., Sorantin, E., Soza, G., Susomboon, R., Waite, J. M., Wimmer, A., & Wolf, I.
- Affiliations
- German Cancer Research Center (DE)
- North Carolina State University (US)
- University Medical Center Utrecht (NL)
- Heinrich Heine University Düsseldorf (DE)
- Graz University of Technology (AT)
- Ludwig-Maximilians-Universität München (DE)
- University of Iowa (US)
- GE Healthcare (Hungary) (HU)
- Imperial College London (GB)
- Clínica Alemana (CL)
- Vanderbilt University (US)
- DePaul University (US)
- Tokyo University of Agriculture and Technology (JP)
- University Hospital Heidelberg (DE)
- Zuse Institute Berlin (DE)
- Charité – Universitätsmedizin Berlin (DE)
- Catholic University of Korea (KR)
- Siemens (United States) (US)
- KU Leuven (BE)
- Medical University of Graz (AT)
- Siemens Healthineers (Germany) (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- ORCID
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2009-08
- Volume
- 28
- Issue
- 8
- Pages
- 1251-1265
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2153431772
- Cited by
- 1,135
- References
- 77
- Citations by year
- 2026: 53 · 2025: 77 · 2024: 61 · 2023: 62 · 2022: 99 · 2021: 93 · 2020: 75 · 2019: 72 · 2018: 56 · 2017: 71 · 2016: 72 · 2015: 75 · 2014: 69 · 2013: 61 · 2012: 52
- FWCI
- 49.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Keywords
- liver segmentation, contrast-enhanced CT, automatic segmentation, interactive segmentation, statistical shape models, atlas registration, level sets, graph cuts, rule-based systems
- MeSH terms
- Algorithms, Bayes Theorem, Humans, Image Processing, Computer-Assisted, Liver, Tomography, X-Ray Computed, Databases, Factual
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
