Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images

In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully convolutional neural networks (FCNNs). First, a global FCNN localizes multiple landmarks through the analysis of image patches, performing regression and classification simultaneously. In regression, displacement vectors pointing from the center of image patches towards landmark locations are determined. In classification, presence of landmarks of interest in the patch is established. Global landmark locations are obtained by averaging the predicted displacement vectors, where the contribution of each displacement vector is weighted by the posterior classification probability of the patch that it is pointing from. Subsequently, for each landmark localized with global localization, local analysis is performed. Specialized FCNNs refine the global landmark locations by analyzing local sub-images in a similar manner, i.e. by performing regression and classification simultaneously and combining the results. Evaluation was performed through localization of 8 anatomical landmarks in CCTA scans, 2 landmarks in olfactory MR scans, and 19 landmarks in cephalometric X-rays. We demonstrate that the method performs similarly to a second observer and is able to localize landmarks in a diverse set of medical images, differing in image modality, image dimensionality, and anatomical coverage.Comment: 12 pages, accepted at IEEE transactions in Medical Imagin

CNN-based Landmark Detection in Cardiac CTA Scans

Fast and accurate anatomical landmark detection can benefit many medical image analysis methods. Here, we propose a method to automatically detect anatomical landmarks in medical images. Automatic landmark detection is performed with a patch-based fully convolutional neural network (FCNN) that combines regression and classification. For any given image patch, regression is used to predict the 3D displacement vector from the image patch to the landmark. Simultaneously, classification is used to identify patches that contain the landmark. Under the assumption that patches close to a landmark can determine the landmark location more precisely than patches farther from it, only those patches that contain the landmark according to classification are used to determine the landmark location. The landmark location is obtained by calculating the average landmark location using the computed 3D displacement vectors. The method is evaluated using detection of six clinically relevant landmarks in coronary CT angiography (CCTA) scans: the right and left ostium, the bifurcation of the left main coronary artery (LM) into the left anterior descending and the left circumflex artery, and the origin of the right, non-coronary, and left aortic valve commissure. The proposed method achieved an average Euclidean distance error of 2.19 mm and 2.88 mm for the right and left ostium respectively, 3.78 mm for the bifurcation of the LM, and 1.82 mm, 2.10 mm and 1.89 mm for the origin of the right, non-coronary, and left aortic valve commissure respectively, demonstrating accurate performance. The proposed combination of regression and classification can be used to accurately detect landmarks in CCTA scans.Comment: This work was submitted to MIDL 2018 Conferenc

Hippocampal volume and integrity as predictors of cognitive decline in intact elderly

Risk of Alzheimer’s disease (AD) can be predicted by volumetric analyses of MRI data in the medial temporal lobe. The present study compared a volumetric measurement of the hippocampus to a novel measure of hippocampal integrity derived from the ratio of parenchyma volume over total volume. Participants were cognitively intact and aged 60 or older at baseline, and were tested twice, roughly three years apart. Participants had been recruited for a study on late-life major 34 depression (LLMD) and were evenly split between depressed and controls. Linear regression models were applied to the data with a cognitive composite score as outcome, and hippocampal integrity (HI) and volume (HV), together or separately, as predictors. Subsequent cognitive performance was predicted well by models that include an interaction between HI and LLMD-status, such that lower HI scores predicted more cognitive decline in depressed subjects. More research is needed, but tentative results from this study appear to suggest that the newly introduced measure HI is an effective tool for the purpose of predicting future changes in general cognitive ability, and especially so in individuals with LLMD

Delineation of hippocampal subregions using T1-weighted magnetic resonance images at 3 Tesla

Although several novel approaches for hippocampal subregion delineation have been developed, they need to be applied prospectively and may be limited by long scan times, the use of high field (\u3e3T) imaging systems, and limited reliability metrics. Moreover, the majority of MR imaging data collected to date has employed a T1-weighted acquisition, creating a critical need for an approach that provides reliable hippocampal subregion segmentation using such a contrast. We present a highly reliable approach for the identification of six subregions comprising the hippocampal formation from MR images including the subiculum, dentate gyrus/cornu Ammonis 4 (DG/CA4), entorhinal cortex, fimbria, and anterior and posterior segments of cornu Ammonis 1-3 (CA1-3). MR images were obtained in the coronal plane using a standard 3D spoiled gradient sequence acquired on a GE 3T scanner through the whole head in approximately 10 min. The average ICC for inter-rater reliability across right and left volumetric regions-of-interest was 0.85 (range 0.71-0.98, median 0.86) and the average ICC for intra-rater reliability was 0.92 (range 0.66-0.99, median 0.97). The mean Dice index for inter-rater reliability across right and left hemisphere subregions was 0.75 (range 0.70-0.81, median 0.75) and the mean Dice index for intra-rater reliability was 0.85 (range 0.82-0.90, median 0.85). An investigation of hippocampal asymmetry revealed significantly greater right compared to left hemisphere volumes in the anterior segment of CA1-3 and in the subiculum

Multi-feature computational framework for combined signatures of dementia in underrepresented settings

Objetivo. El diagnóstico diferencial de la variante conductual de la demencia frontotemporal (bvFTD) y La enfermedad de Alzheimer (EA) sigue siendo un desafío en grupos subrepresentados y subdiagnosticados, incluidos los latinos, ya que los biomarcadores avanzados rara vez están disponibles. Directrices recientes para el estudio de demencia destacan el papel fundamental de los biomarcadores. Por lo tanto, nuevos complementarios rentables Se requieren enfoques en entornos clínicos. Acercarse. Desarrollamos un marco novedoso basado en un clasificador de aprendizaje automático que aumenta el gradiente, ajustado por la optimización bayesiana, en una función múltiple enfoque multimodal (que combina imágenes demográficas, neuropsicológicas y de resonancia magnética) (IRM) y electroencefalografía/datos de conectividad de IRM funcional) para caracterizar neurodegeneración utilizando la armonización del sitio y la selección de características secuenciales. Evaluamos 54 DFTvc y 76 pacientes con EA y 152 controles sanos (HC) de un consorcio latinoamericano (ReDLat). Resultados principales. El modelo multimodal arrojó una alta clasificación de área bajo la curva (pacientes con DFTvc frente a HC: 0,93 (±0,01); pacientes con EA frente a HC: 0,95 (±0,01); DFTvv frente a EA pacientes: 0,92 (±0,01)). El enfoque de selección de características filtró con éxito información no informativa marcadores multimodales (de miles a decenas). Resultados. Probado robusto contra multimodal heterogeneidad, variabilidad sociodemográfica y datos faltantes. Significado. El modelo con precisión subtipos de demencia identificados utilizando medidas fácilmente disponibles en entornos subrepresentados, con un rendimiento similar al de los biomarcadores avanzados. Este enfoque, si se confirma y replica, puede complementar potencialmente las evaluaciones clínicas en los países en desarrollo.Q1Q1Abstract Objective. The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer’s disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings. Approach. We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, magnetic resonance imaging (MRI), and electroencephalography/functional MRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat). Main results. The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens). Results. Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data. Significance. The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.https://orcid.org/0000-0001-6529-7077https://scholar.google.com/citations?hl=es&user=kaGongoAAAAJ&view_op=list_works&sortby=pubdatehttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000055000Revista Internacional - IndexadaS