Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

Abstract

Most automated techniques for brain disease diagnosis utilize hand-crafted (e.g., voxel-based or region-based) biomarkers from structural magnetic resonance (MR) images as feature representations. However, these hand-crafted features are usually high-dimensional or require regions-of-interest defined by experts. Also, because of possibly heterogeneous property between the hand-crafted features and the subsequent model, existing methods may lead to sub-optimal performances in brain disease diagnosis. In this paper, we propose a landmark-based deep feature learning (LDFL) framework to automatically extract patch-based representation from MRI for automatic diagnosis of Alzheimer’s disease. We first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then propose a convolutional neural network for patch-based deep feature learning. We have evaluated the proposed method on subjects from three public datasets, including the Alzheimer’s disease neuroimaging initiative (ADNI-1), ADNI-2, and the minimal interval resonance imaging in alzheimer’s disease (MIRIAD) dataset. Experimental results of both tasks of brain disease classification and MR image retrieval demonstrate that the proposed LDFL method improves the performance of disease classification and MR image retrieval.

Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications

“…Liu et al, focused on another difficult task in using MRI for diagnosis of brain diseases including Alzheimer's disease -that of automatic generation of clinically meaningful features. They proposed a novel approach of identifying discriminative regions called landmarks, followed by application of a CNN for patch-based deep feature learning [10]. Addressing challenges in other common imaging modalities, Gruezemacher et al, developed a three-dimensional (3D) adaptation of deep neural nets (DNNs) for lung nodule detection from computed tomography (CT) images [6]; Hassan et al, presented a novel approach combining tensor-based segmentation, Delauday triangulation, and deep learning models to provide complete 3D presentation of macula to support automated diagnosis of various macula conditions [7].…”

“…Section editors screened this initial list for relevance to the theme and scientific quality, and they rated each paper as "keep," "pend," or "discard." Papers rated as "keep" by one of the section editors were independently reviewed and scored by section editors to yield the top 15 candidate best papers [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Criteria for scoring included innovation beyond established AI techniques, work that addressed substantial challenges in the field, and rigorous scientific evaluations.…”

“…Appendix: Summary of Best Papers Selected for the 2018 Edition of the IMIA Yearbook, Section AI in Health Albers DJ, Levine ME, Stuart A, Mamykina L, Gluckman B, Hripcsak G Mechanistic machine learning: how data assimilation leverages physiological knowledge using bayesian inference to forecast the future, infer the present, and phenotype J Am Med Inform Assoc 2018;25 (10):1392-401…”

Anatomical Landmarks and DAG Network Learning for Alzheimer’s Disease Diagnosis

“…Three public datasets are applied in this study including the Alzheimer's Disease Neuroimaging Initiative (ADNI-1), ADNI-2, and the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset [38]. The subjects include 194 AD and 226 normal controls (NC) with 1.5T T1weighted structure MRI data in the baseline ADNI-1 dataset.…”

Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data

“…The development of machine learning (ML) approaches for computer-aided diagnosis and prognosis of AD has been a very active topic in the past decade, e.g. 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 . In particular, numerous ML methods 14,15,21,28,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47 have been proposed to predict progression to AD among patients with MCI from neuroimaging data (see e.g 48,49,50 for reviews on that topic).…”