Abstract
| Original language | English |
|---|---|
| Title of host publication | Computational Diffusion MRI |
| Subtitle of host publication | International MICCAI Workshop, Granada, Spain, September 2018 |
| Editors | Lipeng Ning, Chantal M.W. Tax, Francesco Grussu, Elisenda Bonet-Carne, Farshid Sepehrband |
| Place of Publication | Cham |
| Publisher | Springer |
| Chapter | 18 |
| Pages | 217-224 |
| Number of pages | 8 |
| Edition | 226249 |
| ISBN (Electronic) | 978-3-030-05831-9 |
| ISBN (Print) | 978-3-030-05830-2 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 16 Sep 2018 → 16 Sep 2018 |
Publication series
| Name | Mathematics and Visualization |
|---|---|
| ISSN (Electronic) | 1612-3786 |
Conference
| Conference | 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 |
|---|---|
| Country | Spain |
| City | Granada |
| Period | 16/09/18 → 16/09/18 |
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Keywords
- Deep learning
- Diffusion MRI
- Harmonisation
- Parametric model
- Spherical harmonics
Cite this
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Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results. / Ning, Lipeng; Bonet-Carne, Elisenda; Grussu, Francesco; Sepehrband, Farshid; Kaden, Enrico; Veraart, Jelle; Blumberg, Stefano B.; Khoo, Can Son; Palombo, Marco; Coll-Font, Jaume; Scherrer, Benoit; Warfield, Simon K. ; Karayumak, Suheyla Cetin; Rathi, Yogesh; Koppers, Simon; Weninger, Leon; Ebert, Julia; Merhof, Dorit; Moyer, Daniel; Pietsch, Maximilian; Christiaens, Daan; Teixeira, Rui; Tournier, Jacques-Donald; Zhylka, Andrey; Pluim, Josien; Parker, Greg; Rudrapatna, Umesh; Evans, John; Charron, Cyril; Jones, Derek K.; Tax, Chantal W. M.
Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018. ed. / Lipeng Ning; Chantal M.W. Tax; Francesco Grussu; Elisenda Bonet-Carne; Farshid Sepehrband. 226249. ed. Cham : Springer, 2019. p. 217-224 (Mathematics and Visualization).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic
TY - GEN
T1 - Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results
AU - Ning, Lipeng
AU - Bonet-Carne, Elisenda
AU - Grussu, Francesco
AU - Sepehrband, Farshid
AU - Kaden, Enrico
AU - Veraart, Jelle
AU - Blumberg, Stefano B.
AU - Khoo, Can Son
AU - Palombo, Marco
AU - Coll-Font, Jaume
AU - Scherrer, Benoit
AU - Warfield, Simon K.
AU - Karayumak, Suheyla Cetin
AU - Rathi, Yogesh
AU - Koppers, Simon
AU - Weninger, Leon
AU - Ebert, Julia
AU - Merhof, Dorit
AU - Moyer, Daniel
AU - Pietsch, Maximilian
AU - Christiaens, Daan
AU - Teixeira, Rui
AU - Tournier, Jacques-Donald
AU - Zhylka, Andrey
AU - Pluim, Josien
AU - Parker, Greg
AU - Rudrapatna, Umesh
AU - Evans, John
AU - Charron, Cyril
AU - Jones, Derek K.
AU - Tax, Chantal W. M.
PY - 2019
Y1 - 2019
N2 - We present a summary of competition results in the multi-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC). MUSHAC is an open competition intended to stimulate the development of computational methods that reduce scanner- and protocol-related variabilities in multi-shell diffusion MRI data across multi-site studies. Twelve different methods from seven research groups have been tested in this challenge. The results show that cross-vendor harmonization and enhancement can be performed by using suitable computational algorithms such as deep convolutional neural networks. Moreover, parametric models for multi-shell diffusion MRI signals also provide reliable performances.
AB - We present a summary of competition results in the multi-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC). MUSHAC is an open competition intended to stimulate the development of computational methods that reduce scanner- and protocol-related variabilities in multi-shell diffusion MRI data across multi-site studies. Twelve different methods from seven research groups have been tested in this challenge. The results show that cross-vendor harmonization and enhancement can be performed by using suitable computational algorithms such as deep convolutional neural networks. Moreover, parametric models for multi-shell diffusion MRI signals also provide reliable performances.
KW - Deep learning
KW - Diffusion MRI
KW - Harmonisation
KW - Parametric model
KW - Spherical harmonics
UR - http://www.scopus.com/inward/record.url?scp=85066914373&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-05831-9_18
DO - 10.1007/978-3-030-05831-9_18
M3 - Conference contribution
SN - 978-3-030-05830-2
T3 - Mathematics and Visualization
SP - 217
EP - 224
BT - Computational Diffusion MRI
A2 - Ning, Lipeng
A2 - Tax, Chantal M.W.
A2 - Grussu, Francesco
A2 - Bonet-Carne, Elisenda
A2 - Sepehrband, Farshid
PB - Springer
CY - Cham
ER -