Development and Validation of a Deep Learning–Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images

BACKGROUND AND PURPOSE:Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. Our aim was to develop and validate a deep learning–based automatic brain segmentation and classification algorithm for the diagnosis of Alzheimer disease using 3D T1-weighted brain MR images.MATERIALS AND METHODS:A deep learning–based algorithm was developed using a dataset of T1-weighted brain MR images in consecutive patients with Alzheimer disease and mild cognitive impairment. We developed a 2-step algorithm using a convolutional neural network to perform brain parcellation followed by 3 classifier techniques including XGBoost for disease prediction. All classification experiments were performed using 5-fold cross-validation. The diagnostic performance of the XGBoost method was compared with logistic regression and a linear Support Vector Machine by calculating their areas under the curve for differentiating Alzheimer disease from mild cognitive impairment and mild cognitive impairment from healthy controls.RESULTS:In a total of 4 datasets, 1099, 212, 711, and 705 eligible patients were included. Compared with the linear Support Vector Machine and logistic regression, XGBoost significantly improved the prediction of Alzheimer disease (P < .001). In terms of differentiating Alzheimer disease from mild cognitive impairment, the 3 algorithms resulted in areas under the curve of 0.758–0.825. XGBoost had a sensitivity of 68% and a specificity of 70%. In terms of differentiating mild cognitive impairment from the healthy control group, the 3 algorithms resulted in areas under the curve of 0.668–0.870. XGBoost had a sensitivity of 79% and a specificity of 80%.CONCLUSIONS:The deep learning–based automatic brain segmentation and classification algorithm allowed an accurate diagnosis of Alzheimer disease using T1-weighted brain MR images. The widespread availability of T1-weighted brain MR imaging suggests that this algorithm is a promising and widely applicable method for predicting Alzheimer disease.

Alzheimer disease (AD) is the most common cause of dementia, with mild cognitive impairment (MCI) regarded as a transitional state between normal cognition and early stages of dementia.¹ Although current therapeutic and preventive options are only moderately effective, a reliable decision-making diagnostic approach is important during early stages of AD.^2,3 The guidelines of the National Institute on Aging–Alzheimer’s Association suggest that MR imaging is a supportive imaging tool in the diagnostic work-up of patients with AD and MCI.^2,3 Imaging biomarkers play an important role in the diagnosis of AD, both in the research field and in clinical practice. The identification of amyloid and the τ PET ligand provided huge advances in understanding the pathophysiologic mechanisms underlying AD and its early diagnosis, even in the preclinical or prodromal stage.^4-6 Although amyloid and τ PET are more sensitive and specific for the diagnosis of AD, they are expensive to perform, have limited availability, and require ionizing radiation, limiting their use in clinical practice. CSF amyloid and τ are also important biomarkers that could be used for AD diagnostics in the clinical research setting.^3,7-9 However, CSF AD biomarkers also have limited availability. MR imaging, however, is widely available and used in standard practice to support the diagnosis of AD and to exclude other causes of cognitive impairment, including stroke, vascular dementia, normal-pressure hydrocephalus, and inflammatory and neoplastic conditions.3D T1-weighted volumetric MR imaging is the most important MR imaging tool in the diagnosis of AD. 3D volumetry has long been used as a morphologic diagnostic tool for AD, not only as a visual assessment or manual segmentation but for semiautomatic and automatic segmentation. Examples include semiautomatic structural changes on MR imaging,¹⁰ automated hippocampal volumetry,¹¹ entorhinal cortex atrophy,¹² and changes in pineal gland volume.¹³ Although user-friendly automated segmentation algorithms were first introduced 20 years ago, evidence supporting the use of 3D volumetry in clinical practice is currently insufficient. Visual assessment requires experience, and automatic 3D volumetry requires a long acquisition time.To our knowledge, limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict AD.¹⁴ Currently available algorithms have low clinical feasibility because of the long processing time for brain segmentation, and the classification algorithm based on T1-weighted brain MR images needs to be validated in a large external dataset. The purpose of this study was to develop and validate a deep learning–based automatic brain segmentation and classification algorithm for the diagnosis of AD using 3D T1-weighted brain MR images.