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Eric Westman

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17 papers

2 author rows

YNIMG Journal 2021 Journal Article

Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

Meera Srikrishna
Joana B. Pereira
Rolf A. Heckemann
Giovanni Volpe
Danielle van Westen
Anna Zettergren
Silke Kern
Lars-Olof Wahlund

Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research.

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IROS Conference 2020 Conference Paper

A Theory of Fermat Paths for 3D Imaging Sonar Reconstruction

Eric Westman
Ioannis Gkioulekas
Michael Kaess

In this work, we present a novel method for reconstructing particular 3D surface points using an imaging sonar sensor. We derive the two-dimensional Fermat flow equation, which may be applied to the planes defined by each discrete azimuth angle in the sonar image. We show that the Fermat flow equation applies to boundary points and surface points which correspond to specular reflections within the 2D plane defined by their azimuth angle measurement. The Fermat flow equation can be used to resolve the 2D location of these surface points within the plane, and therefore also their full 3D location. This is achieved by translating the sensor to estimate the spatial gradient of the range measurement. This method does not rely on the precise image intensity values or the reflectivity of the imaged surface to solve for the surface point locations. We demonstrate the effectiveness of our proposed method by reconstructing 3D object points on both simulated and real-world datasets.

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ICRA Conference 2020 Conference Paper

A Volumetric Albedo Framework for 3D Imaging Sonar Reconstruction

Eric Westman
Ioannis Gkioulekas
Michael Kaess

We present a novel framework for object-level 3D underwater reconstruction using imaging sonar sensors. We demonstrate that imaging sonar reconstruction is analogous to the problem of confocal non-line-of-sight (NLOS) reconstruction. Drawing upon this connection, we formulate the problem as one of solving for volumetric albedo, where the scene of interest is modeled as a directionless albedo field. After discretization, reconstruction reduces to a convex linear optimization problem, which we can augment with a variety of priors and regularization terms. We show how to solve the resulting regularized problems using the alternating direction method of multipliers (ADMM) algorithm. We demonstrate the effectiveness of the proposed approach in simulation and on real-world datasets collected in a controlled, test tank environment with several different sonar elevation apertures.

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YNIMG Journal 2020 Journal Article

Cholinergic white matter pathways make a stronger contribution to attention and memory in normal aging than cerebrovascular health and nucleus basalis of Meynert

Milan Nemy
Nira Cedres
Michel J. Grothe
J-Sebastian Muehlboeck
Olof Lindberg
Zuzana Nedelska
Olga Stepankova
Lenka Vyslouzilova

The integrity of the cholinergic system plays a central role in cognitive decline both in normal aging and neurological disorders including Alzheimer's disease and vascular cognitive impairment. Most of the previous neuroimaging research has focused on the integrity of the cholinergic basal forebrain, or its sub-region the nucleus basalis of Meynert (NBM). Tractography using diffusion tensor imaging data may enable modelling of the NBM white matter projections. We investigated the contribution of NBM volume, NBM white matter projections, small vessel disease (SVD), and age to performance in attention and memory in 262 cognitively normal individuals (39-77 years of age, 53% female). We developed a multimodal MRI pipeline for NBM segmentation and diffusion-based tracking of NBM white matter projections, and computed white matter hypointensities (WM-hypo) as a marker of SVD. We successfully tracked pathways that closely resemble the spatial layout of the cholinergic system as seen in previous post-mortem and DTI tractography studies. We found that high WM-hypo load was associated with older age, male sex, and lower performance in attention and memory. A high WM-hypo load was also associated with lower integrity of the cholinergic system above and beyond the effect of age. In a multivariate model, age and integrity of NBM white matter projections were stronger contributors than WM-hypo load and NBM volume to performance in attention and memory. We conclude that the integrity of NBM white matter projections plays a fundamental role in cognitive aging. This and other modern neuroimaging methods offer new opportunities to re-evaluate the cholinergic hypothesis of cognitive aging.

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YNICL Journal 2020 Journal Article

Medial temporal atrophy in preclinical dementia: Visual and automated assessment during six year follow-up

Gustav Mårtensson
Claes Håkansson
Joana B. Pereira
Sebastian Palmqvist
Oskar Hansson
Danielle van Westen
Eric Westman

Medial temporal lobe (MTL) atrophy is an important morphological marker of many dementias and is closely related to cognitive decline. In this study we aimed to characterize longitudinal progression of MTL atrophy in 93 individuals with subjective cognitive decline and mild cognitive impairment followed up over six years, and to assess if clinical rating scales are able to detect these changes. All MRI images were visually rated according to Scheltens' scale of medial temporal atrophy (MTA) by two neuroradiologists and AVRA, a software for automated MTA ratings. The images were also segmented using FreeSurfer's longitudinal pipeline in order to compare the MTA ratings to volumes of the hippocampi and inferior lateral ventricles. We found that MTL atrophy rates increased with CSF biomarker abnormality, used to define preclinical stages of Alzheimer's Disease. Both AVRA's and the radiologists' MTA ratings showed similar longitudinal trends as the subcortical volumes, suggesting that visual rating scales provide a valid alternative to automatic segmentations. Our results further showed that it took more than 8 years on average for individuals with mild cognitive impairment, and an Alzheimer's disease biomarker profile, to increase the MTA score by one. This suggests that discrete MTA ratings are too coarse for tracking individual MTL atrophy in short time spans. While the MTA scores from each radiologist showed strong correlations to subcortical volumes, the inter-rater agreement was low. We conclude that the main limitation of quantifying MTL atrophy with visual ratings in clinics is the subjectiveness of the assessment.

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YNIMG Journal 2020 Journal Article

Morphological changes in secondary, but not primary, sensory cortex in individuals with life-long olfactory sensory deprivation

Moa G. Peter
Gustav Mårtensson
Elbrich M. Postma
Love Engström Nordin
Eric Westman
Sanne Boesveldt
Johan N. Lundström

Individuals with congenital sensory deprivation usually demonstrate altered brain morphology in areas associated with early processing of the absent sense. Here, we aimed to establish whether this also applies to individuals born without a sense of smell (congenital anosmia) by comparing cerebral morphology between 33 individuals with isolated congenital anosmia and matched controls. We detected no morphological alterations in the primary olfactory (piriform) cortex. However, individuals with anosmia demonstrated gray matter volume atrophy in bilateral olfactory sulci, explained by decreased cortical area, curvature, and sulcus depth. They further demonstrated increased gray matter volume and cortical thickness in the medial orbital gyri; regions closely associated with olfactory processing, sensory integration, and value-coding. Our results suggest that a lifelong absence of sensory input does not necessarily lead to morphological alterations in primary sensory cortex and extend previous findings with divergent morphological alterations in bilateral orbitofrontal cortex, indicating influences of different developmental processes.

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YNICL Journal 2020 Journal Article

The combined effect of amyloid-β and tau biomarkers on brain atrophy in dementia with Lewy bodies

Carla Abdelnour
Daniel Ferreira
Ketil Oppedal
Lena Cavallin
Olivier Bousiges
Lars Olof Wahlund
Jakub Hort
Zuzana Nedelska

BACKGROUND: Alzheimer's disease (AD)-related pathology is frequently found in patients with dementia with Lewy bodies (DLB). However, it is unknown how amyloid-β and tau-related pathologies influence neurodegeneration in DLB. Understanding the mechanisms underlying brain atrophy in DLB can improve our knowledge about disease progression, differential diagnosis, drug development and testing of anti-amyloid and anti-tau therapies in DLB. OBJECTIVES: We aimed at investigating the combined effect of CSF amyloid-β42, phosphorylated tau and total tau on regional brain atrophy in DLB in the European DLB (E-DLB) cohort. METHODS: 86 probable DLB patients from the E-DLB cohort with CSF and MRI data were included. Random forest was used to analyze the association of CSF biomarkers (predictors) with visual rating scales for medial temporal lobe atrophy (MTA), posterior atrophy (PA) and global cortical atrophy scale-frontal subscale (GCA-F) (outcomes), including age, sex, education and disease duration as extra predictors. RESULTS: DLB patients with abnormal MTA scores had abnormal CSF Aβ42, shorter disease duration and older age. DLB patients with abnormal PA scores had abnormal levels of CSF Aβ42 and p-tau, older age, lower education and shorter disease duration. Abnormal GCA-F scores were associated with lower education, male sex, and older age, but not with any AD-related CSF biomarker. CONCLUSIONS: This study shows preliminary data on the potential combined effect of amyloid-β and tau-related pathologies on the integrity of posterior brain cortices in DLB patients, whereas only amyloid-β seems to be related to MTA. Future availability of α-synuclein biomarkers will help us to understand the effect of α-synuclein and AD-related pathologies on brain integrity in DLB.

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YNICL Journal 2019 Journal Article

AVRA: Automatic visual ratings of atrophy from MRI images using recurrent convolutional neural networks

Gustav Mårtensson
Daniel Ferreira
Lena Cavallin
J-Sebastian Muehlboeck
Lars-Olof Wahlund
Chunliang Wang
Eric Westman

Quantifying the degree of atrophy is done clinically by neuroradiologists following established visual rating scales. For these assessments to be reliable the rater requires substantial training and experience, and even then the rating agreement between two radiologists is not perfect. We have developed a model we call AVRA (Automatic Visual Ratings of Atrophy) based on machine learning methods and trained on 2350 visual ratings made by an experienced neuroradiologist. It provides fast and automatic ratings for Scheltens' scale of medial temporal atrophy (MTA), the frontal subscale of Pasquier's Global Cortical Atrophy (GCA-F) scale, and Koedam's scale of Posterior Atrophy (PA). We demonstrate substantial inter-rater agreement between AVRA's and a neuroradiologist ratings with Cohen's weighted kappa values of κ w = 0. 74/0. 72 (MTA left/right), κ w = 0. 62 (GCA-F) and κ w = 0. 74 (PA). We conclude that automatic visual ratings of atrophy can potentially have great scientific value, and aim to present AVRA as a freely available toolbox.

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IROS Conference 2019 Conference Paper

Wide Aperture Imaging Sonar Reconstruction using Generative Models

Eric Westman
Michael Kaess

In this paper we propose a new framework for reconstructing underwater surfaces from wide aperture imaging sonar sequences. We demonstrate that when the leading object edge in each sonar image can be accurately triangulated in 3D, the remaining surface may be “filled in” using a generative sensor model. This process generates a full three-dimensional point cloud for each image in the sequence. We propose integrating these surface measurements into a cohesive global map using a truncated signed distance field (TSDF) to fuse the point clouds generated by each image. This allows for reconstructing surfaces with significantly fewer sonar images and viewpoints than previous methods. The proposed method is evaluated by reconstructing a mock-up piling structure and a real world underwater piling, in a test tank environment and in the field, respectively. Our surface reconstructions are quantitatively compared to ground-truth models and are shown to be more accurate than previous state-of-the-art algorithms.

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ICRA Conference 2018 Conference Paper

Dense Planar-Inertial SLAM with Structural Constraints

Ming Hsiao
Eric Westman
Michael Kaess

In this work, we develop a novel dense planar-inertial SLAM (DPI-SLAM) system to reconstruct dense 3D models of large indoor environments using a hand-held RGB-D sensor and an inertial measurement unit (IMU). The preinte-grated IMU measurements are loosely-coupled with the dense visual odometry (VO) estimation and tightly-coupled with the planar measurements in a full SLAM framework. The poses, velocities, and IMU biases are optimized together with the planar landmarks in a global factor graph using incremental smoothing and mapping with the Bayes Tree (iSAM2). With odometry estimation using both RGB-D and IMU data, our system can keep track of the poses of the sensors even without sufficient planes or visual information (e. g. textureless walls) temporarily. Modeling planes and IMU states in the fully probabilistic global optimization reduces the drift that distorts the reconstruction results of other SLAM algorithms. Moreover, structural constraints between nearby planes (e. g. right angles) are added into the DPI-SLAM system, which further recovers the drift and distortion. We test our DPI-SLAM on large indoor datasets and demonstrate its state-of-the-art performance as the first planar-inertial SLAM system.

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ICRA Conference 2018 Conference Paper

Feature-Based SLAM for Imaging Sonar with Under-Constrained Landmarks

Eric Westman
Akshay Hinduja
Michael Kaess

Recent algorithms have demonstrated the feasibility of underwater feature-based SLAM using imaging sonar. But previous methods have either relied on manual feature extraction and correspondence or used prior knowledge of the scene, such as the planar scene assumption. Our proposed system provides a general-purpose method for feature-point extraction and correspondence in arbitrary scenes. Additionally, we develop a method of identifying point landmarks that are likely to be well-constrained and reliably reconstructed. Finally, we demonstrate that while under-constrained landmarks cannot be accurately reconstructed themselves, they can still be used to constrain and correct the sensor motion. These advances represent a large step towards general-purpose, feature-based SLAM with imaging sonar.

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IROS Conference 2018 Conference Paper

Information Sparsification in Visual-Inertial Odometry

Jerry Hsiung
Ming Hsiao
Eric Westman
Rafael Valencia
Michael Kaess

In this paper, we present a novel approach to tightly couple visual and inertial measurements in a fixed-lag visual-inertial odometry (VIO) framework using information sparsification. To bound computational complexity, fixed-lag smoothers typically marginalize out variables, but consequently introduce a densely connected linear prior which significantly deteriorates accuracy and efficiency. Current state-of-the-art approaches account for the issue by selectively discarding measurements and marginalizing additional variables. However, such strategies are sub-optimal from an information-theoretic perspective. Instead, our approach performs a dense marginalization step and preserves the information content of the dense prior. Our method sparsifies the dense prior with a nonlinear factor graph by minimizing the information loss. The resulting factor graph maintains information sparsity, structural similarity, and nonlinearity. To validate our approach, we conduct real-time drone tests and perform comparisons to current state-of-the-art fixed-lag VIO methods in the EuRoC visual-inertial dataset. The experimental results show that the proposed method achieves competitive and superior accuracy in almost all trials. We include a detailed run-time analysis to demonstrate that the proposed algorithm is suitable for real-time applications.

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ICRA Conference 2017 Conference Paper

Keyframe-based dense planar SLAM

Ming Hsiao
Eric Westman
Guofeng Zhang 0001
Michael Kaess

In this work, we develop a novel keyframe-based dense planar SLAM (KDP-SLAM) system, based on CPU only, to reconstruct large indoor environments in real-time using a hand-held RGB-D sensor. Our keyframe-based approach applies a fast dense method to estimate odometry, fuses depth measurements from small baseline images, extracts planes from the fused depth map, and optimizes the poses of the keyframes and landmark planes in a global factor graph using incremental smoothing and mapping (iSAM). Using the fast odometry estimation, correct plane correspondences may be found projectively, and the pose of each frame can be estimated accurately even without sufficient planes to fully constrain the 6 degree-of-freedom transformation. The depth map generated from the local fusion process generates higher quality reconstructions and plane segmentations by eliminating noise. Moreover, explicitly modeling plane landmarks in the fully probabilistic global optimization significantly reduces the drift that plagues other dense SLAM algorithms. We test our system on standard RGB-D benchmarks as well as additional indoor environments, demonstrating its state-of-the-art performance as a real-time dense 3D SLAM algorithm, without the use of GPU.

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YNICL Journal 2017 Journal Article

Monitoring disease progression in mild cognitive impairment: Associations between atrophy patterns, cognition, APOE and amyloid

Farshad Falahati
Daniel Ferreira
J-Sebastian Muehlboeck
Maria Eriksdotter
Andrew Simmons
Lars-Olof Wahlund
Eric Westman

BACKGROUND: A disease severity index (SI) for Alzheimer's disease (AD) has been proposed that summarizes MRI-derived structural measures into a single score using multivariate data analysis. OBJECTIVES: To longitudinally evaluate the use of the SI to monitor disease progression and predict future progression to AD in mild cognitive impairment (MCI). Further, to investigate the association between longitudinal change in the SI and cognitive impairment, Apolipoprotein E (APOE) genotype as well as the levels of cerebrospinal fluid amyloid-beta 1-42 (Aβ) peptide. METHODS: The dataset included 195 AD, 145 MCI and 228 control subjects with annual follow-up for three years, where 70 MCI subjects progressed to AD (MCI-p). For each subject the SI was generated at baseline and follow-ups using 55 regional cortical thickness and subcortical volumes measures that extracted by the FreeSurfer longitudinal stream. RESULTS: = 0.004). CONCLUSIONS: Longitudinal changes in the SI reflect structural brain changes and can identify MCI patients at risk of progression to AD. Disease-related brain structural changes are influenced independently by APOE genotype and amyloid pathology. The SI has the potential to be used as a sensitive tool to predict future dementia, monitor disease progression as well as an outcome measure for clinical trials.

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YNIMG Journal 2012 Journal Article

Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion

Eric Westman
J-Sebastian Muehlboeck
Andrew Simmons

The suggested revision of the NINCDS–ADRDA criterion for the diagnosis of Alzheimer's disease (AD) includes at least one abnormal biomarker among magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF). We aimed to investigate if the combination of baseline MRI and CSF could enhance the classification of AD compared to using either alone and predict mild cognitive impairment (MCI) conversion at multiple future time points. 369 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI) were included in the study (AD=96, MCI=162 and CTL=111). Freesurfer was used to generate regional subcortical volumes and cortical thickness measures. A total of 60 variables were used for orthogonal partial least squares to latent structures (OPLS) multivariate analysis (57 MRI measures and 3 CSF measures: Aβ42, t-tau and p-tau). Combining MRI and CSF gave the best results for distinguishing AD vs. CTL. We found an accuracy of 91. 8% for the combined model at baseline compared to 81. 6% for CSF measures and 87. 0% for MRI measures alone. The combined model also gave the best accuracy when distinguishing between MCI vs. CTL (77. 6%) at baseline. MCI subjects who converted to AD by 12 and 18month follow-up were accurately predicted at baseline using an AD vs. CTL model (82. 9% and 86. 4% respectively), with lower prediction accuracies for those MCI subjects converting by 24 and 36month follow up (75. 4% and 68. 0% respectively). The overall prediction accuracies for converters and non-converters ranged from 58. 6% to 66. 4% at different time points. Combining MRI and CSF measures in a multivariate model at baseline gave better accuracy for discriminating between AD and CTL, between MCI and CTL and for predicting future conversion from MCI to AD, than using either MRI or CSF separately.

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YNIMG Journal 2011 Journal Article

AddNeuroMed and ADNI: Similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America

Eric Westman
Andrew Simmons
J-Sebastian Muehlboeck
Patrizia Mecocci
Bruno Vellas
Magda Tsolaki
Iwona Kłoszewska
Hilkka Soininen

The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimer's disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.

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YNIMG Journal 2011 Journal Article

Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls

Eric Westman
Andrew Simmons
Yi Zhang
J-Sebastian Muehlboeck
Catherine Tunnard
Yawu Liu
Louis Collins
Alan Evans

We have used multivariate data analysis, more specifically orthogonal partial least squares to latent structures (OPLS) analysis, to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI) and elderly control subjects combining both regional and global magnetic resonance imaging (MRI) volumetric measures. In this study, 117 AD patients, 122 MCI patients and 112 control subjects (from the AddNeuroMed study) were included. High-resolution sagittal 3D MP-RAGE datasets were acquired from each subject. Automated regional segmentation and manual outlining of the hippocampus were performed for each image. Altogether this yielded volumes of 24 different anatomically defined structures which were used for OPLS analysis. 17 randomly selected AD patients, 12 randomly selected control subjects and the 22 MCI subjects who converted to AD at 1-year follow up were excluded from the initial OPLS analysis to provide a small external test set for model validation. Comparing AD with controls we found a sensitivity of 87% and a specificity of 90% using hippocampal measures alone. Combining both global and regional measures resulted in a sensitivity of 90% and a specificity of 94%. This increase in sensitivity and specificity resulted in an increase of the positive likelihood ratio from 9 to 15. From the external test set, the model predicted 82% of the AD patients and 83% of the control subjects correctly. Finally, 73% of the MCI subjects which converted to AD at 1year follow-up were shown to resemble AD patients more closely than controls. This method shows potential for distinguishing between different patient groups. Combining the different MRI measures together resulted in a significantly better classification than using them separately. OPLS also shows potential for predicting conversion from MCI to AD.

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