Adrià Casamitjana

Adrià Casamitjana

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 2

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Blog Post number 1

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portfolio

publications

Bayesian learning for time-varying linear prediction of speech

Published in European Signal Processing Conference (EUSIPCO), 2015

Abstract

We develop Bayesian learning algorithms for estimation of time-varying linear prediction (TVLP) coefficients of speech. Estimation of TVLP coefficients is a naturally underdetermined problem. We consider sparsity and subspace based approaches for dealing with the corresponding underdetermined system. Bayesian learning algorithms are developed to achieve better estimation performance. Expectationmaximization (EM) framework is employed to develop the Bayesian learning algorithms where we use a combined prior to model a driving noise (glottal signal) that has both sparse and dense statistical properties. The efficiency of the Bayesian learning algorithms is shown for synthetic signals using spectral distortion measure and formant tracking of real speech signals.

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3D Convolutional Neural Networks for Brain Tumor Segmentation; A Comparison of Multi-resolution Architectures

Published in MICCAI BrainLes Workshop, 2017

Abstract

This paper analyzes the use of 3D Convolutional Neural Networks for brain tumor segmentation in MR images. We address the problem using three different architectures that combine fine and coarse features to obtain the final segmentation. We compare three different networks that use multi-resolution features in terms of both design and performance and we show that they improve their single-resolution counterparts.

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Voxelwise nonlinear regression toolbox for neuroimage analysis; Application to aging and neurodegenerative disease modeling

Published in NIPS Workshop Machine Learning for Health (ML4H), 2017

Abstract

This paper describes a new neuroimaging analysis toolbox that allows for the modeling of nonlinear effects at the voxel level, overcoming limitations of methods based on linear models like the GLM. We illustrate its features using a relevant example in which distinct nonlinear trajectories of Alzheimer’s disease related brain atrophy patterns were found across the full biological spectrum of the disease. The open-source toolbox is available in GitHub: https://github.com/imatge-upc/VNeAT.

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MRI-based screening of preclinical Alzheimer`s disease for prevention clinical trials

Published in Journal of Alzheimer\`s Disease, 2018

Abstract

Introduction: The identification of healthy individuals harboring amyloid pathology constitutes one important challenge for secondary prevention clinical trials in Alzheimers disease. Consequently, noninvasive and cost-efficient techniques to detect preclinical AD constitute an unmet need of critical importance. Methods: We apply machine learning to structural MRI (T1 and DWI) to identify amyloid-positive subjects. Models were trained on public ADNI data and validated on an independent local cohort. Results: Used for subject classification in a simulated clinical trial setting, the proposed method is able to save 60% unnecessary CSF/PET tests and to reduce 47% of the cost of recruitment when used in a simulated clinical trial setting. Discussion: This recruitment strategy capitalizes on already acquired MRIs to reduce the overall amount of invasive PET/CSF tests in prevention trials, demonstrating a potential value as a tool for AD screening. This protocol could foster the development of secondary prevention strategies for AD.

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Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms; The iSeg-2017 Challenge

Published in IEEE Transactions in Medical Imaging (TMI), 2019

Abstract

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.

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Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge

Published in IEEE Transaction in Medical Imaging (TMI), 2019

Abstract

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://wmh.isi.uu.nl/). Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner robustness. Twenty participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.

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Prediction of amyloid pathology in cognitively unimpaired individuals using voxelwise analysis of longitudinal structural brain MRI

Published in Alzheimer’s\`s Research and Therapy, 2019

Abstract

Background: Magnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer`s disease (AD) pathophysiologic continuum constituting what has been established as ‘AD signature’. To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration. Method: Longitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cut-offs (<192pg/mL) to establish diagnostic categories. Cognitively unimpaired individuals were defined as Ctrls if were amyloid negative and PreAD otherwise. The MCI/AD group was amyloid positive. Only subjects with the same diagnostic category at baseline and follow-up visits were considered for the study. Longitudinal morphometric analysis was performed using SPM12 to calculate Jacobian determinant maps. Statistical analysis was carried out on these jacobian maps to identify structural changes that were significantly different between diagnostic categories. A machine learning classifier was applied on Jacobian determinant maps to predict the presence of abnormal amyloid levels in cognitively unimpaired individuals. The performance of this classifier was evaluated using receiver operating characteristic curve analysis and as a function of the follow-up time between MRI scans. We applied a cost function to assess the benefit of using this classifier in the triaging of individuals in a clinical trial-recruitment setting. Results: The optimal follow-up time for classification of Ctrls vs PreAD was Δt>2.5 years and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC=0.87 (95%CI:0.72-0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior and lateral temporal lobes, precuneus, caudate heads, basal forebrain and lateral ventricles. Conclusions: Our work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid positive individuals, this longitudinal voxelwise classifier is expected to avoid 55% of unnecessary CSF and/or PET scans and reduce economic cost by 40%.

Shared latent structures between imaging features and biomarkers in early stages of Alzheimer`s disease; a predictive study

Published in Journal of Biomedical and Health Informatics (J-BHI), 2019

Abstract

Magnetic resonance imaging (MRI) provides high resolution brain morphological information and is used as a biomarker in neurodegenerative diseases. Population studies of brain morphology often seek to identify pathological structural changes related to different diagnostic categories (e.g: controls, mild cognitive impairment or dementia) which normally describe highly heterogeneous groups with a single categorical variable. Instead, multiple biomarkers are used as a proxy for pathology and are more powerful in capturing structural variability. Hence, using the joint modeling of brain morphology and biomarkers, we aim at describing structural changes related to any brain condition by means of few underlying processes. In this regard, we use a multivariate approach based on Projection to Latent Structures in its regression variant (PLSR) to study structural changes related to aging and AD pathology. MRI volumetric and cortical thickness measurements are used for brain morphology and cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau and amyloid-beta) are used as a proxy for AD pathology. By relating both sets of measurements, PLSR finds a low-dimensional latent space describing AD pathological effects on brain structure. The proposed framework allows to separately model aging effects on brain morphology as a confounder variable orthogonal to the pathological effect. The predictive power of the associated latent spaces (i.e. the capacity of predicting biomarker values) is assessed in a cross-validation framework.

NeAT. A nonlinear analysis toolbox for neuroimaging

Published in Neuroinformatics, 2020

Abstract

NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects over-coming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a widerange of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting andcomplexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on twostudy cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer’sdisease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed athttps://imatge-upc.github.io/neat-tool/.

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A multimodal computational pipeline for 3D histology of the human brain

Published in Scientific reports - Nature Publishing Group, 2020

Abstract

Ex vivoimaging enables analysis of the human brain at a level of detail that is not possibleinvivowith MRI. In particular, histology can be used to study brain tissue at the microscopiclevel, using a wide array of different stains that highlight different microanatomical features.Complementing MRI with histology has important applications inex vivoatlas building andin modeling the link between microstructure and macroscopic MR signal. However, histologyrequires sectioning tissue, hence distorting its 3D structure, particularly in larger human samples.Here, we present an open-source computational pipeline to produce 3D consistent histologyreconstructions of the human brain. The pipeline relies on a volumetric MRI scan that serves asundistorted reference, and on an intermediate imaging modality (blockface photography) thatbridges the gap between MRI and histology. We present results on 3D histology reconstructionof a whole human hemisphere.

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Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction. Application to the Allen human brain atlas

Published in Medical Image Analysis, 2021

Abstract

Joint registration of a stack of 2D histological sections to recover 3D structure (“3D histology reconstruction”) finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as “banana effect” (straightening of curved structures) and “z-shift” (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be seen as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (L2-norm, which can be minimised in closed form) and Laplacian (L1-norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest.

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Synth-by-Reg (SbR). Contrastive learning for synthesis-based registration of paired images

Published in International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI (MICCAI-21 Satellite Event), 2021

Abstract

Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment. Here we propose a synthesis-by-registration method to convert this prob- lem into an easier intra-modality task. We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data. This loss capitalises on a regis- tration U-Net with frozen weights, to drive a synthesis CNN towards the desired translation. We complement this loss with a structure preserv- ing constraint based on contrastive learning, which prevents blurring and content shifts due to overfitting. We apply this method to the registration of histological sections to MRI slices, a key step in 3D histology recon- struction. Results on two public datasets show improvements over regis- tration based on mutual information (13% reduction in landmark error) and synthesis-based algorithms such as CycleGAN (11% reduction), and are comparable to registration with label supervision. Code and data are publicly available at https://github.com/acasamitjana/SynthByReg.

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High-resolution atlasing and segmentation of the subcortex. Review and perspective on challenges and opportunities created by machine learning

Published in Neuroimage, 2022

Abstract

This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.

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3D Reconstruction and Segmentation of Dissection Photographs for MRI-Free Neuropathology

Published in International Conference on Medical Image Computing and Computer-Assisted Intervention, 20120

Abstract

Neuroimaging to neuropathology correlation (NTNC) promis-es to enable the transfer of microscopic signatures of pathology to in vivo imaging with MRI, ultimately enhancing clinical care. NTNC traditionally requires a volumetric MRI scan, acquired either ex vivo or a short time prior to death. Unfortunately, ex vivo MRI is difficult and costly, and recent premortem scans of sufficient quality are seldom available. To bridge this gap, we present methodology to 3D reconstruct and segment full brain image volumes from brain dissection photographs, which are routinely acquired at many brain banks and neuropathology departments. The 3D reconstruction is achieved via a joint registration framework, which uses a reference volume other than MRI. This volume may represent either the sample at hand (e.g., a surface 3D scan) or the general population (a probabilistic atlas). In addition, we present a Bayesian method to segment the 3D reconstructed photographic volumes into 36 neuroanatomical structures, which is robust to nonuniform brightness within and across photographs. We evaluate our methods on a dataset with 24 brains, using Dice scores and volume correlations. The results show that dissection photography is a valid replacement for ex vivo MRI in many volumetric analyses, opening an avenue for MRI-free NTNC, including retrospective data. The code is available at https://github.com/htregidgo/DissectionPhotoVolumes.

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resources

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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