Biomarker for tracking progression of Alzheimer's disease in clinical trials

Currently, there are no treatments available for mitigating the neurological effects of Alzheimer's disease. All clinical trials of disease-modifying treatments, which showed promise in animal models, have failed to show a significant treatment effect in human trials. The lack of a sensitive outcome measure and the focus on the dementia stage for investigating treatments are believed to be the primary reasons behind the failure of all clinical trials till date. The currently used outcome measure, the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog), suffers from low sensitivity in tracking progression of cognitive impairment in clinical trials. A shift in the focus to the prodromal mild cognitive impairment (MCI) stage may help improve the efficiency of clinical trials. However, even lower sensitivity of the ADAS-Cog and an inability to specifically select progressive MCI patients limit the efficiency of clinical trials in the MCI stage. Cerebral atrophy measured on structural magnetic resonance (MR) imaging is highly promising for tracking disease progression in clinical trials. However, cerebral atrophy has not been yet approved as a valid biomarker due to the lack of an understanding behind its relationship with cognitive impairment. The focus of this dissertation spans across the two research areas of (i) developing automatic algorithms for analysis of patients' brain MR volumes, and (ii) improving the efficiency of clinical trials of disease-modifying treatments. This dissertation presents a novel knowledge-driven decision theory approach for automatic tissue segmentation of brain MR volumes, which shows better segmentation performance than the existing approaches. The remaining dissertation contributions focus at improving the efficiency of clinical trials of disease-modifying treatments. An improved scoring methodology is presented for the ADAS-Cog outcome measure, which measures cognitive impairment with better accuracy and significantly improves the sensitivity of the ADAS-Cog in the mild-to-moderate Alzheimer's disease stage. However, the ADAS-Cog continues to suffers from low sensitivity in the MCI stage due to inherent limitations of its items. For improving the efficiency of clinical trials in the MCI stage, a biomarker has been developed that combines the ADAS-Cog with cerebral atrophy for more accurate tracking of Alzheimer's progression and facilitating selection of MCI patients in clinical trials.Biomedical Engineerin

Adaptive pixon represented segmentation (APRS) for 3D MR brain images based on mean shift and Markov random fields

International audienceIn this paper, we proposed an adaptive pixon represented segmentation (APRS) algorithm for 3D magnetic resonance (MR) brain images. Different from traditional method, an adaptive mean shift algorithm was adopted to adaptively smooth the query image and create a pixon-based image representation. Then K-means algorithm was employed to provide an initial segmentation by classifying the pixons in image into a predefined number of tissue classes. By using this segmentation as initialization, expectation-maximization (EM) iterations composed of bias correction, a priori digital brain atlas information, and Markov random field (MRF) segmentation were processed. Pixons were assigned with final labels when the algorithm converges. The adoption of bias correction and brain atlas made the current method more suitable for brain image segmentation than the previous pixon based segmentation algorithm. The proposed method was validated on both simulated normal brain images from BrainWeb and real brain images from the IBSR public dataset. Compared with some other popular MRI segmentation methods, the proposed method exhibited a higher degree of accuracy in segmenting both simulated and real 3D MRI brain data. The experimental results were numerically assessed using Dice and Tanimoto coefficients