1Department of Information Engineering, University of Padova, Padova, Italy
2Institute of Radiology, Department of Medicine-DIMED, University of Padova, Padova, Italy
3Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova, Italy
Presenting Author: Ambra Checchetto
Synopsis
Motivation:
Goals:
Approach:
Results:
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.
Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.
To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.
1. Kirk P, Roughton M, Porter JB, Walker JM, Tanner MA, Patel J, et al. Cardiac T2* Magnetic Resonance for Prediction of Cardiac Complications in Thalassemia Major. Circulation. 2009;120:1961‐1968. https://doi.org/10.1161/CIRCULATIONAHA.109.874487 [doi]
2. Wood JC, Noetzli L. Cardiovascular MRI in thalassemia major. Ann N Y Acad Sci. 2010;1202:173‐179. https://doi.org/10.1111/j.1749-6632.2010.05571.x [doi]
3. Anderson LJ. Assessment of iron overload with T2* magnetic resonance imaging. Prog Cardiovasc Dis. 2011;54:287‐294. https://doi.org/10.1016/j.pcad.2011.07.004 [doi]
4. Wood JC. Impact of Iron Assessment by MRI. Hematol Am Soc Hematol Educ Program. 2011;2011:443‐450. https://doi.org/10.1182/asheducation-2011.1.443 [doi]
5. Anderson LJ, Holden S, Davis B, Prescott E, Charrier CC, Bunce NH, et al. Cardiovascular T2-star (T2*) magnetic resonance for the early diagnosis of myocardial iron overload. Eur Heart J. 2001;22:2171‐2179. https://doi.org/10.1053/euhj.2001.2822 [doi]
6. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60‐88. https://doi.org/10.1016/j.media.2017.07.005 [doi]
7. Lian Z, Lu Q, Peng P, Lin B, Chen L, Feng Y. MRI Deep Learning-Based Automatic Segmentation of Interventricular Septum for Black-Blood Myocardial T2* Measurement in Thalassemia. J Magn Reson Imaging. 2024;60:651‐661. https://doi.org/10.1002/jmri.29113 [doi]
8. Wantanajittikul K, Theera-Umpon N, Saekho S, Auephanwiriyakul S, Phrommintikul A, Leemasawat K. Automatic cardiac T2* relaxation time estimation from magnetic resonance images using region growing method with automatically initialized seed points. Comput Methods Programs Biomed. 2016;130:76‐86. https://doi.org/10.1016/j.cmpb.2016.03.015 [doi]
9. Zheng Q, Feng Y, Wei X, Feng M, Chen W, Lu Z, Xu Y, Chen H, He T. Automated interventricular septum segmentation for black-blood myocardial T2* measurement in thalassemia. J Magn Reson Imaging. 2015;41:1242‐1250. https://doi.org/10.1002/jmri.24662 [doi]
10. Zheng Q, Lu Z, Zhang M, Xu L, Ma H, Song S, Feng Q, Feng Y, Chen W, He T. Automatic Segmentation of Myocardium from Black-Blood MR Images Using Entropy and Local Neighborhood Information. PLoS One. 2015;10(3):e0120018. https://doi.org/10.1371/journal.pone.0120018 [doi]
11. Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, Pennell DJ, Rumberger JA, Ryan T, Verani MS. Standardized Myocardial Segmentation and Nomenclature for Tomographic Imaging of the Heart. Circulation. 2002;105:539‐542. https://doi.org/10.1161/hc0402.102975 [doi]
12. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nn-UNet: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods. 2021;18(2), 203-211. https://doi.org/10.1038/s41592-020-01008-z [doi]
13. Ma J, Li F, Wang B. U-Mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722. Published 2024. https://doi.org/10.48550/arXiv.2401.04722 [doi]
14. Wasserthal J, Breit HC, Meyer MT, Pradella M, Hinck D, Sauter AW, Heye T, Boll D, Cyriac J, Yang S, Bach M, Segeroth M. TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiology: Artificial Intelligence. 2023. https://doi.org/10.1148/ryai.230024 [doi]
15. Warfield SK, Zou KH, Wells WM. Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation. IEEE Trans Med Imaging. 2004;23(7):903‐921. doi:10.1109/TMI.2004.828354 [doi]
16. Liu Q, Deng H, Lian C, Chen X, Xiao D, Ma L, Chen X, Kuang T, Gateno J, Yap PT, et al. SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection. Mach Learn Med Imaging. 2021; 12966: 606–614. doi:10.1007/978-3-030-87589-3_62 [doi]
17. Positano V, Pepe A, Santarelli MF, Scattini B, De Marchi D, Ramazzotti A, Forni G, Borgna-Pignatti C, Lai ME, Midiri M, Maggio A, Lombardi M, Landini L. Standardized T2* map of normal human heart in vivo to correct T2* segmental artefacts. NMR Biomed. 2007;20:578‐590. https://doi.org/10.1002/nbm.1121 [doi]