Detecting Anatomical Landmarks for Fast Alzheimer's Disease Diagnosis

Abstract

Structural magnetic resonance imaging (MRI) is a very popular and effective technique used to diagnose Alzheimer's disease (AD). The success of computer-aided diagnosis methods using structural MRI data is largely dependent on the two time-consuming steps: 1) nonlinear registration across subjects, and 2) brain tissue segmentation. To overcome this limitation, we propose a landmark-based feature extraction method that does not require nonlinear registration and tissue segmentation. In the training stage, in order to distinguish AD subjects from healthy controls (HCs), group comparisons, based on local morphological features, are first performed to identify brain regions that have significant group differences. In general, the centers of the identified regions become landmark locations (or AD landmarks for short) capable of differentiating AD subjects from HCs. In the testing stage, using the learned AD landmarks, the corresponding landmarks are detected in a testing image using an efficient technique based on a shape-constrained regression-forest algorithm. To improve detection accuracy, an additional set of salient and consistent landmarks are also identified to guide the AD landmark detection. Based on the identified AD landmarks, morphological features are extracted to train a support vector machine (SVM) classifier that is capable of predicting the AD condition. In the experiments, our method is evaluated on landmark detection and AD classification sequentially. Specifically, the landmark detection error (manually annotated versus automatically detected) of the proposed landmark detector is 2.41 mm , and our landmark-based AD classification accuracy is 83.7%. Lastly, the AD classification performance of our method is comparable to, or even better than, that achieved by existing region-based and voxel-based methods, while the proposed method is approximately 50 times faster.

Fig. 1

Diagram illustrating the steps in the proposed landmark detection and AD classification framework. In general, the proposed framework defines three sequential steps: 1) landmark definition, 2) landmark detection, and 3) AD/HC classification.

Fig. 2

AD landmark definition pipeline.

Fig. 3

Saliency, inconsistency and active maps. Each map is linearly stretched to [0,1] for clear visualization. (a) Saliency map, where regions with larger values are more salient than those with smaller ones. (b) Inconsistency map, where regions with larger values are more inconsistent than those with smaller ones. (c) Combined active map, where regions with larger values are more active than those with smaller ones.

Fig. 4

Framework of regression-forest-based landmark detection. (a) Definition of displacement from a voxel to a target landmark. (b) Regression voting. (c) Voting map.

Fig. 5

Targets for regression forest. (a) Targets using traditional displacements to multiple landmarks. (b) Targets using a shape constraint.

Fig. 6

Active-landmark guided AD landmark detection. (a) Definition of displacements to the active landmarks and AD landmarks. (b) Framework of two-layer regression-forest-based landmark detection.

Fig. 7

Group comparison results for two datasets, D1 and D2. Regions with very small p-values (i.e., having statistically significant difference) are shown in blue.

Fig. 8

Detection errors for active landmarks and AD landmarks. (a) Detection errors with different numbers of partition groups. (b) Cumulative distribution with different error intervals where 10 groups are clustered.

Fig. 9

AD/HC classification accuracy on ADNI-1. (a) Classification accuracy with respect to different patch sizes, where the horizontal axis means the side length of the cubic patch. (b) Classification accuracy with respect to different landmark detection strategies and group numbers, where the red star is the result of registration-based landmark mapping.