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| 40 | + <title>VSMOD: A Vessel Segmentation and MODelization plugin for 3D Slicer</title> |
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| 155 | + <journal_title>Journal of Open Source Software</journal_title> |
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| 174 | + <article_title>Robust RANSAC-based blood vessel segmentation</article_title> |
| 175 | + <author>Yureidini</author> |
| 176 | + <journal_title>Medical Imaging 2012: Image Processing</journal_title> |
| 177 | + <volume>8314</volume> |
| 178 | + <doi>10.1117/12.911670</doi> |
| 179 | + <cYear>2012</cYear> |
| 180 | + <unstructured_citation>Yureidini, A., Kerrien, E., & Cotin, S. (2012). Robust RANSAC-based blood vessel segmentation. Medical Imaging 2012: Image Processing, 8314, 474–481. https://doi.org/10.1117/12.911670</unstructured_citation> |
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| 183 | + <article_title>An effective interactive medical image segmentation method using fast GrowCut</article_title> |
| 184 | + <author>Zhu</author> |
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| 187 | + <unstructured_citation>Zhu, L., Kolesov, I., Gao, Y., Kikinis, R., & Tannenbaum, A. (2014). An effective interactive medical image segmentation method using fast GrowCut. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Interactive Medical Image Computing Workshop.</unstructured_citation> |
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| 192 | + <journal_title>Computerized Medical Imaging and Graphics</journal_title> |
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| 196 | + <unstructured_citation>Rougé, P., Conze, P.-H., Passat, N., & Merveille, O. (2025). Guidelines for cerebrovascular segmentation: Managing imperfect annotations in the context of semi-supervised learning. Computerized Medical Imaging and Graphics, 119, 102474. https://doi.org/10.1016/j.compmedimag.2024.102474</unstructured_citation> |
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| 201 | + <journal_title>Topology- and graph-informed imaging informatics</journal_title> |
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| 205 | + <unstructured_citation>Carneiro-Esteves, S., Vacavant, A., & Merveille, O. (2025). Restoring connectivity in vascular segmentations using a learned post-processing model. In C. Chen, Y. Singh, & X. Hu (Eds.), Topology- and graph-informed imaging informatics (pp. 55–65). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-73967-5_6</unstructured_citation> |
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| 210 | + <journal_title>PLOS ONE</journal_title> |
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| 215 | + <unstructured_citation>Rougé, P., Passat, N., & Merveille, O. (2024). Topology aware multitask cascaded U-Net for cerebrovascular segmentation. PLOS ONE, 19(12), 1–20. https://doi.org/10.1371/journal.pone.0311439</unstructured_citation> |
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| 217 | + <citation key="Keshwani2020"> |
| 218 | + <article_title>TopNet: Topology preserving metric learning for vessel tree reconstruction and labelling</article_title> |
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| 220 | + <journal_title>Medical image computing and computer assisted intervention – MICCAI 2020</journal_title> |
| 221 | + <doi>10.1007/978-3-030-59725-2_2</doi> |
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| 223 | + <cYear>2020</cYear> |
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