There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
Principal Investigator | |
Principal Investigator's Name: | Michael Coopman |
Institution: | University of Texas at Arlington |
Department: | Mathematics |
Country: | |
Proposed Analysis: | The ADNI data will be used to test a method for medical image registration. In particular, we plan to replicate and refine the method performed in a joint paper between Tsinghua University and University of Texas at Arlington. This method uses Voxelmorph, a framework for performing image registration through deep learning. Typically, the loss function for registration is based on a similarity term and a smoothness term. The method outlined replaces the smoothness term with the Laplacian of the transformation. Then, by defining a control function to the Laplacian, a more consistent transformation can be created. According to the paper, this resulted in a better mean Dice score than Voxelmorph registration methods. The paper referred to is the following: Yongpei Zhu, Zicong Zhou Sr., Guojun Liao Sr., and Kehong Yuan "New loss functions for medical image registration based on VoxelMorph", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132E (10 March 2020); https://doi.org/10.1117/12.2550030 |
Additional Investigators | |
Investigator's Name: | Guojun Liao |
Proposed Analysis: | The images being requested will be used to test a method for medical image registration. In particular, we plan to replicate and refine the method performed in a joint paper between Tsinghua University and University of Texas at Arlington. Typically, the loss function of registration is based on a similarity term and a smoothness term. The method outlined replaces the smoothness term with the Laplacian of the transformation. Then, by defining a control function to the Laplacian, a more consistent transformation can be created. According to the paper, this resulted in a better mean Dice score than other Voxelmorph registration methods. The paper referred to is the following: Yongpei Zhu, Zicong Zhou Sr., Guojun Liao Sr., and Kehong Yuan "New loss functions for medical image registration based on VoxelMorph", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132E (10 March 2020); https://doi.org/10.1117/12.2550030 |