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: | Juanjuan Wang |
| Institution: | Zhengzhou University |
| Department: | School of Electrical Engineering |
| Country: | |
| Proposed Analysis: | The purpose of our research is to use deep learning methods to achieve automatic segmentation of MRI images. In neuroimaging research, brain MRI segmentation is often a key preprocessing step. In clinical medicine, automatic segmentation of MRI images can extract structural information features, such as volume and shape, which may be used to assess the condition of the subject. We guarantee to comply with ADNI data usage related agreements, and hope to obtain data support from ADNI. |
| Additional Investigators | |
| Investigator's Name: | Shouhao Li |
| Proposed Analysis: | The purpose of our research is to use deep learning methods to achieve automatic segmentation of MRI images. In neuroimaging research, brain MRI segmentation is often a key preprocessing step. In clinical medicine, automatic segmentation of MRI images can extract structural information features, such as volume and shape, which may be used to assess the condition of the subject. We guarantee to comply with ADNI data usage related agreements, and hope to obtain data support from ADNI. |
| Investigator's Name: | Yun Liu |
| Proposed Analysis: | The purpose of our research is to use deep learning methods to achieve automatic segmentation of MRI images. In neuroimaging research, brain MRI segmentation is often a key preprocessing step. In clinical medicine, automatic segmentation of MRI images can extract structural information features, such as volume and shape, which may be used to assess the condition of the subject. We guarantee to comply with ADNI data usage related agreements, and hope to obtain data support from ADNI. |
| Investigator's Name: | Yun Liu |
| Proposed Analysis: | The purpose of our research is to use deep learning methods to achieve automatic segmentation of MRI images. In neuroimaging research, brain MRI segmentation is often a key preprocessing step. In clinical medicine, automatic segmentation of MRI images can extract structural information features, such as volume and shape, which may be used to assess the condition of the subject. We guarantee to comply with ADNI data usage related agreements, and hope to obtain data support from ADNI. |
| Investigator's Name: | Xiaoyuan Li |
| Proposed Analysis: | The purpose of our research is to realize automatic segmentation of MRI image by using deep learning method. In neuroimaging research, brain MRI segmentation is often a key preprocessing step. In clinical medicine, MRI image automatic segmentation can extract structural information features, such as volume and shape, which may be used to evaluate the condition of subjects. We promise to abide by the ADNI data usage protocol and hope to get the data support from ADNI. |
| Investigator's Name: | Liqiang Zou |
| Proposed Analysis: | The purpose of our research is to realize automatic segmentation of MRI image by using deep learning method. In neuroimaging research, brain MRI segmentation is often a key preprocessing step. In clinical medicine, MRI image automatic segmentation can extract structural information features, such as volume and shape, which may be used to evaluate the condition of subjects. We promise to abide by the ADNI data usage protocol and hope to get the data support from ADNI. |
