The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation
Abstract
A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to the segmentation of individual organs and struggle to deal with the variability of the shape and position of abdominal organs. We present a general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans. The method is based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlas registration and patch-based segmentation, two widely used methods in brain segmentation. The final segmentation is obtained by applying an automatically learned intensity model in a graph-cuts optimization step, incorporating high-level spatial knowledge. The proposed approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. We have evaluated the segmentation on a database of 150 manually segmented CT images. The achieved results compare well to state-of-the-art methods, that are usually tailored to more specific questions, with Dice overlap values of 94%, 93%, 70%, and 92% for liver, kidneys, pancreas, and spleen, respectively.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
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
Self-citationRandom Walks for Image Segmentation
Grady, L. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.233 · Cited by 2,624
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
Comparison and evaluation of methods for liver segmentation from CT datasets
Heimann, T., van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2013851 · Cited by 1,135
Fast free-form deformation using graphics processing units
Modat, M., Ridgway, G. R., Taylor, Z. A., Lehmann, M., Barnes, J., Hawkes, D. J., Fox, N. C., & Ourselin, S. (2010) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2009.09.002 · Cited by 1,085
Automatic 3‐D model‐based neuroanatomical segmentation
Collins, D. L., Holmes, C. J., Peters, T. M., & Evans, A. C. (1995) · Human Brain Mapping · doi:10.1002/hbm.460030304 · Cited by 974
Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy
Aljabar, P., Heckemann, R. A., Hammers, A., Hajnal, J. V., & Rueckert, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.02.018 · Cited by 868
Self-citationAutomatic anatomical brain MRI segmentation combining label propagation and decision fusion
Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., & Hammers, A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.05.061 · Cited by 866
Self-citationPatch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation
Coupé, P., Manjón, J. V., Fonov, V., Pruessner, J., Robles, M., & Collins, D. L. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.018 · Cited by 692
Cited by
Attention U-Net: Learning Where to Look for the Pancreas
Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1804.03999 · Cited by 5,458
Self-citationAttention gated networks: learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014
Self-citationDeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
Roth, H. R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E. B., & Summers, R. M. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24553-9_68 · Cited by 784
Multi-atlas segmentation of biomedical images: A survey
Iglesias, J. E., & Sabuncu, M. R. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.06.012 · Cited by 667
Roth, H. R., Lu, L., Lay, N., Harrison, A. P., Farag, A., Sohn, A., & Summers, R. M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.01.006 · Cited by 325
Metadata
- Authors
- Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K., & Rueckert, D.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2013-09
- Volume
- 32
- Issue
- 9
- Pages
- 1723-1730
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2000585255
- Cited by
- 273
- References
- 31
- FWCI
- 22.30
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation. https://app.cochat.ai/papers/10.1109/tmi.2013.2265805
- The paper
- Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K., & Rueckert, D. (2013). Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2013.2265805
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
