Automated segmentation of hippocampal subfields from ultra‐high resolution in vivo MRI
By K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, Lawrence L. Wald, Jean C. Augustinack, Bradford C. Dickerson, Polina Golland, Bruce Fischl. Hippocampus, 2009
439 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/8281846c96730d51a360b90f3aa3d5dd19b0d408
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
In Computer Vision and Pattern Recognition
- Juan Eugenio Iglesias
- Bruce Fischl
- Lawrence L. Wald
- K. V. Leemput
- Martin Reuter
- Mert Rory Sabuncu
- H. Rosas
- Douglas N. Greve
In Radiology, Nuclear Medicine and Imaging
- Bruce Fischl
- Juan Eugenio Iglesias
- Lawrence L. Wald
- K. V. Leemput
- Jean C. Augustinack
- Ender Konukoglu
- Ben Glocker
- Mert Rory Sabuncu
Citations in the corpus
- A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI (2015, positive): “If ~ α is the current estimate of α , the bound is: where W i , nm , k is given by the E Step: It can easily be shown that the maximum of Q α ; ~ α ð Þ is attained at (M step): As mentioned previously, the optimization scheme in Van Leemput (2009) is recovered when the mapping from L to C is bijecti”
- FreeSurfer (2012, methodology): “…volumetric segmentation of most macroscopically visible brain structures (Fischl et al., 2002, 2004a), segmentation of hippocampal subfields (Van Leemput et al., 2009), inter-subject alignment based on cortical folding patterns (Fischl et al., 1999b), segmentation of white matter fascicles…”