BrainWeb: Online Interface to a 3D MRI Simulated Brain Database. Introduction: The increased importance of automated computer techniques for anatomical brain mapping from MR images and quantitative brain image analysis methods leads to an increased need for validation and evaluation of the effect of image acquisition parameters on performance of these procedures. Validation of analysis techniques of in-vivo acquired images is complicated due to the lack of reference data (”ground truth”). Also, optimal selection of the MR imaging parameters is difficult due to the large parameter space. BrainWeb makes available to the neuroimaging community, online on WWW, a set of realistic simulated brain MR image volumes (Simulated Brain Database, SBD) that allows the above issues to be examined in a controlled, systematic way. Methods: The 3D simulated MR images are generated by varying specific imaging parameters and artifacts in an MRI simulator, which: ffl starts from a fuzzy digital phantom containing the spatial pro.

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  1. Dong, Guozhi; Hintermüller, Michael; Papafitsoros, Kostas: Optimization with learning-informed differential equation constraints and its applications (2022)
  2. Song, Jianhua; Yuan, Lei: Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field (2022)
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  4. Zeng, Chao; Jiang, Tai-Xiang; Ng, Michael K.: An approximation method of CP rank for third-order tensor completion (2021)
  5. Carlos Gavidia-Calderon; César Beltrán Castañon: Isula: A java framework for ant colony algorithms (2020) not zbMATH
  6. Curtin, Lee; Hawkins-Daarud, Andrea; Porter, Alyx B.; van der Zee, Kristoffer G.; Owen, Markus R.; Swanson, Kristin R.: A mechanistic investigation into ischemia-driven distal recurrence of glioblastoma (2020)
  7. Gao, Yiming; Wu, Chunlin: On a general smoothly truncated regularization for variational piecewise constant image restoration: construction and convergent algorithms (2020)
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  9. Perrillat-Mercerot, Angélique; Miranville, Alain; Bourmeyster, Nicolas; Guillevin, Carole; Naudin, Mathieu; Guillevin, Rémy: What mathematical models can or cannot do in glioma description and understanding (2020)
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  14. Song, Jianhua; Zhang, Zhe: Brain tissue segmentation and bias field correction of MR image based on spatially coherent FCM with nonlocal constraints (2019)
  15. Subramanian, Shashank; Gholami, Amir; Biros, George: Simulation of glioblastoma growth using a 3D multispecies tumor model with mass effect (2019)
  16. Chakraborty, Shouvik; Mali, Kalyani: Application of multiobjective optimization techniques in biomedical image segmentation -- a study (2018)
  17. Datta, Niladri Sekhar; Dutta, Himadri Sekhar; Majumder, Koushik; Chatterjee, Sumana; Wasim, Najir Abdul: A survey on the application of multi-objective optimization methods in image segmentation (2018)
  18. Mang, Andreas; Gholami, Amir; Davatzikos, Christos; Biros, George: PDE-constrained optimization in medical image analysis (2018)
  19. Pawar, Aishwarya; Zhang, Yongjie Jessica; Anitescu, Cosmin; Jia, Yue; Rabczuk, Timon: DTHB3D_Reg: dynamic truncated hierarchical B-spline based 3D nonrigid image registration (2018)
  20. Rasch, Julian; Brinkmann, Eva-Maria; Burger, Martin: Joint reconstruction via coupled Bregman iterations with applications to PET-MR imaging (2018)

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