Probabilistic model for 3D interactive segmentation
•Interactive 3D medical image segmentation based on a Bayesian inference is suggested.•User-machine “dialogue” is allowed by a few mouse clicks in regions of disagreement.•User input is formulated as a probabilistic spatial term in a level-set functional.•A generic method which accommodates differen...
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| Published in: | Computer vision and image understanding Vol. 151; pp. 47 - 60 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.10.2016
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| ISSN: | 1077-3142, 1090-235X |
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| Abstract | •Interactive 3D medical image segmentation based on a Bayesian inference is suggested.•User-machine “dialogue” is allowed by a few mouse clicks in regions of disagreement.•User input is formulated as a probabilistic spatial term in a level-set functional.•A generic method which accommodates different modalities, e.g. CT and multimodal MRI.•GUI for clinical uses allows real-time, high performance with minimal user feedback.
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Fully-automated segmentation algorithms offer fast, objective, and reproducible results for large data collections. However, these techniques cannot handle tasks that require contextual knowledge not readily available in the images alone. Thus, the supervision of an expert is necessary.
We present a generative model for image segmentation, based on a Bayesian inference. Not only does our approach support an intuitive and convenient user interaction subject to the bottom-up constraints introduced by the image intensities, it also circumvents the main limitations of a human observer—3D visualization and modality fusion. The user “dialogue” with the segmentation algorithm via several mouse clicks in regions of disagreement, is formulated as a continuous probability map, that represents the user’s certainty to whether the current segmentation should be modified. Considering this probability map as the voxel-vise Bernoulli priors on the image labels allows spatial encoding of the user-provided input. The method is exemplified for the segmentation of cerebral hemorrhages (CH) in human brain CT scans; ventricles in degenerative mice brain MRIs, and tumors in multi-modal human brain MRIs and is shown to outperform three interactive, state-of-the-art segmentation methods in terms of accuracy, efficiency and user-workload. |
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| AbstractList | •Interactive 3D medical image segmentation based on a Bayesian inference is suggested.•User-machine “dialogue” is allowed by a few mouse clicks in regions of disagreement.•User input is formulated as a probabilistic spatial term in a level-set functional.•A generic method which accommodates different modalities, e.g. CT and multimodal MRI.•GUI for clinical uses allows real-time, high performance with minimal user feedback.
[Display omitted]
Fully-automated segmentation algorithms offer fast, objective, and reproducible results for large data collections. However, these techniques cannot handle tasks that require contextual knowledge not readily available in the images alone. Thus, the supervision of an expert is necessary.
We present a generative model for image segmentation, based on a Bayesian inference. Not only does our approach support an intuitive and convenient user interaction subject to the bottom-up constraints introduced by the image intensities, it also circumvents the main limitations of a human observer—3D visualization and modality fusion. The user “dialogue” with the segmentation algorithm via several mouse clicks in regions of disagreement, is formulated as a continuous probability map, that represents the user’s certainty to whether the current segmentation should be modified. Considering this probability map as the voxel-vise Bernoulli priors on the image labels allows spatial encoding of the user-provided input. The method is exemplified for the segmentation of cerebral hemorrhages (CH) in human brain CT scans; ventricles in degenerative mice brain MRIs, and tumors in multi-modal human brain MRIs and is shown to outperform three interactive, state-of-the-art segmentation methods in terms of accuracy, efficiency and user-workload. |
| Author | Kahn, Itamar Shelef, Ilan Riklin Raviv, Tammy Shalmon, Tamar Dolgopyat, Irit Shitrit, Ohad Hershkovich, Tsachi Halay, Nir Menze, Bjoern H. |
| Author_xml | – sequence: 1 givenname: Tsachi surname: Hershkovich fullname: Hershkovich, Tsachi organization: Electrical and Computer Engineering Department, Ben-Gurion University, Beer-Sheva, Israel – sequence: 2 givenname: Tamar surname: Shalmon fullname: Shalmon, Tamar organization: The Zlotowski Center for Neuroscience, Ben-Gurion University, Beer-Sheva, Israel – sequence: 3 givenname: Ohad surname: Shitrit fullname: Shitrit, Ohad organization: Electrical and Computer Engineering Department, Ben-Gurion University, Beer-Sheva, Israel – sequence: 4 givenname: Nir surname: Halay fullname: Halay, Nir organization: Electrical and Computer Engineering Department, Ben-Gurion University, Beer-Sheva, Israel – sequence: 5 givenname: Bjoern H. surname: Menze fullname: Menze, Bjoern H. organization: Department of Computer Science, TU Munchen, Munich, Germany – sequence: 6 givenname: Irit surname: Dolgopyat fullname: Dolgopyat, Irit organization: Rappaport Faculty of Medicine, Technion, Haifa, Israel – sequence: 7 givenname: Itamar surname: Kahn fullname: Kahn, Itamar organization: Rappaport Faculty of Medicine, Technion, Haifa, Israel – sequence: 8 givenname: Ilan surname: Shelef fullname: Shelef, Ilan organization: The Zlotowski Center for Neuroscience, Ben-Gurion University, Beer-Sheva, Israel – sequence: 9 givenname: Tammy orcidid: 0000-0003-2532-5875 surname: Riklin Raviv fullname: Riklin Raviv, Tammy email: rrtammy@ee.bgu.ac.il, tammy@csail.mit.edu organization: Electrical and Computer Engineering Department, Ben-Gurion University, Beer-Sheva, Israel |
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| Cites_doi | 10.1016/S0896-6273(03)00627-5 10.1016/j.mri.2012.05.001 10.1109/TPAMI.2012.183 10.1016/j.media.2005.09.002 10.1111/j.2517-6161.1977.tb01600.x 10.2307/1932409 10.1038/nature13186 10.1006/gmip.1998.0480 10.1109/TMI.2013.2274734 10.1016/j.neuroimage.2005.02.018 10.1117/12.708609 10.1145/1015706.1015720 10.1007/BF00133570 10.1109/42.906424 10.1109/TMI.2014.2377694 10.1007/s11548-013-0922-7 10.1016/j.media.2012.06.002 10.1109/TMI.2015.2502596 10.1109/TVCG.2010.208 10.1137/S0036144598347059 10.1016/j.compmedimag.2008.07.004 10.1016/j.media.2010.05.004 10.1023/A:1010933404324 10.1016/0021-9991(88)90002-2 10.1007/s11263-006-9042-y 10.1109/83.902291 10.1006/gmip.1998.0475 10.1109/34.969114 10.1016/j.media.2013.08.004 10.1007/s11263-006-8711-1 |
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| Keywords | Image segmentation Probabilistic model Modality fusion User interaction MR & CT Brain imaging |
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