Roberto Souza

The University of Calgary · Electrical and Software Engineering

Identifying biomarkers for serious mental illnesses (SMI) has significant implications for prevention and early intervention. In the current study, changes in whole brain structural and functional connectomes were investigated in youth at transdiagnostic risk over a one-year period. Based on clinical assessments, participants were assigned to one o...

Our motion correction algorithm is aimed toward neonatal brain Magnetic Resonance Imaging (MRI). This would benefit researchers and clinical practitioners in overcoming motion artifacts in neonatal scans. Neonatal brain MRIs are frequently compromised by motion artifacts, resulting in low-quality outputs and scan interruptions. While popular Deep L...

Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes, which if harnessed appropriately, can contribute to advancements in various sectors, from healthcare to economics. However, its black box nature presents significant ethical challenges related to bias and transparency. AI applications are hugely impacted by...

Brain aging is a regional phenomenon, a facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods. Voxel-level predictions can provide localized brain age estimates that can provide granular insights into the regional aging processes. This is essential to understand the differences...

Purpose To investigate whether parallel imaging‐imposed geometric coil constraints can be relaxed when using a deep learning (DL)‐based image reconstruction method as opposed to a traditional non‐DL method. Theory and Methods Traditional and DL‐based MR image reconstruction approaches operate in fundamentally different ways: Traditional methods so...

Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan. There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo...

Simulation studies, such as finite element (FE) modeling, provide insight into knee joint mechanics without patient involvement. Generic FE models mimic the biomechanical behavior of the tissue, but overlook variations in geometry, loading, and material properties of a population. Conversely, subject-specific models include these factors, resulting...

Choroidal nevi are difficult to identify and require regular eye screening. While high-resolution fundus images are commonly used to identify choroidal nevi, manual review is time-consuming and requires specialized knowledge. Deep learning shows promise for accurate classification of eye diseases. However, these models require extensive labell...

The rapid and constant development of deep learning (DL) strategies is pushing forward the quality of object segmentation in images from diverse fields of interest. In particular, these algorithms can be very helpful in delineating brain abnormalities (lesions, tumors, lacunas, etc), enabling the extraction of information such as volume and locatio...

Global brain age estimation has been used as an effective biomarker to study the correlation between brain aging and neurological disorders. However, it fails to provide spatial information on the brain aging process. Voxel-level brain age estimation can give insights into how different regions of the brain age in a diseased versus healthy brain. W...

Edited magnetic resonance spectroscopy (MRS) can provide localized information on gamma-aminobutyric acid (GABA) concentration in vivo. However, edited-MRS scans are long due to the fact that many acquisitions, known as transients, need to be collected and averaged to obtain a high-quality spectrum for reliable GABA quantification. In this work, we...

While utilizing machine learning models, one of the most crucial aspects is how bias and fairness affect model outcomes for diverse demographics. This becomes especially relevant in the context of machine learning for medical imaging applications as these models are increasingly being used for diagnosis and treatment planning. In this paper, we stu...

Purpose To create a benchmark for the comparison of machine learning-based Gamma-Aminobutyric Acid (GABA)-edited Magnetic Resonance Spectroscopy (MRS) reconstruction models using one quarter of the transients typically acquired during a complete scan. Methods The Edited-MRS reconstruction challenge had three tracks with the purpose of evaluating ma...

We present MedShapeNet, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D surgical instrument models. Prior to the deep learning era, the broad application of statistical shape models (SSMs) in medical image analysis is evidence that shapes have been commonly used to describe medical data. Nowadays, however, state-of-the...

Deep learning models have achieved state-of-the-art results in estimating brain age, which is an important brain health biomarker, from magnetic resonance (MR) images. However, most of these models only provide a global age prediction, and rely on techniques, such as saliency maps to interpret their results. These saliency maps highlight regions in...

Most visual impairment and eye cancers are preventable if detected in their early stages. Diabetic retinopathy (DR) is a significant cause of blindness worldwide and a serious public health concern in a population aged 20–65. With the increasing number of diabetes globally and its effects on patients’ vision, the automatic detection of DR has recei...

To determine the significance of complex-valued inputs and complex-valued convolutions compared to real-valued inputs and real-valued convolutions in Convolutional Neural Networks (CNNs) for frequency and phase correction (FPC) of GABA-edited Magnetic Resonance Spectroscopy (MRS) data. An ablation study was performed to determine the most effective...

Federated learning (FL) is a distributed learning paradigm that preserves users’ data privacy while leveraging the entire dataset of all participants. In FL, multiple models are trained independently on the clients and aggregated centrally to update a global model in an iterative process. Although this approach is excellent at preserving privacy, F...

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addi...

Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques for this task. Nevertheless, the segmentations produced by machine learning models often degrade in the presence of expected domain shifts between the t...

Background: Identifying early biomarkers of serious mental illness (SMI)—such as changes in brain structure and function—can aid in early diagnosis and treatment. Whole brain structural and functional connectomes were investigated in youth at risk for SMI. Methods: Participants were classified as healthy controls (HC; n=33), familial risk for seri...

COVID-19 pandemic has resulted in excess mortality globally and presented an unprecedented challenge to people's lives. Despite the benefits of getting a COVID-19 vaccine, there have been arguments against the available vaccines and vaccine hesitancy worldwide. In this work, we analyze the information published by the public on Reddit as a digital...

Diabetic retinopathy (DR) is known as an important cause of blindness worldwide and serious public health concern in the population aged 20–65. With the burgeoning number of diabetes globally and its effects on patients' vision, the automatic detection of DR has received wide attention from the machine learning field. However, due to the black-box...

Supervised deep learning methods have shown great promise for making magnetic resonance (MR) imaging scans faster. However, these supervised deep learning models need large volumes of labelled data to learn valuable representations and produce high-fidelity MR image reconstructions. The data used to train these models are often fully-sampled raw MR...

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the pr...

RGB-D data is essential for solving many problems in computer vision. Hundreds of public RGB-D datasets containing various scenes, such as indoor, outdoor, aerial, driving, and medical, have been proposed. These datasets are useful for different applications and are fundamental for addressing classic computer vision tasks, such as monocular depth e...

Youth at clinical high risk (CHR) for psychosis can present not only with characteristic attenuated psychotic symptoms but also may have other comorbid conditions, including anxiety and depression. These undifferentiated mood symptoms can overlap with the clinical presentation of youth with Distress syndromes. Increased resting-state functional con...

RGB-D data is essential for solving many problems in computer vision. Hundreds of public RGB-D datasets containing various scenes, such as indoor, outdoor, aerial, driving, and medical, have been proposed. These datasets are useful for different applications and are fundamental for addressing classic computer vision tasks, such as monocular depth e...

Federated learning (FL) is a widely adopted distributed learning paradigm in practice, which intends to preserve users' data privacy while leveraging the entire dataset of all participants for training. In FL, multiple models are trained independently on the users and aggregated centrally to update a global model in an iterative process. Although t...

Purpose To develop a deep‐learning model that leverages the spatial and temporal information from dynamic contrast‐enhanced magnetic resonance (DCE MR) brain imaging in order to automatically estimate a vascular function (VF) for quantitative pharmacokinetic (PK) modeling. Methods Patients with glioblastoma multiforme were scanned post‐resection a...

The 2020 Multi-channel Magnetic Resonance Reconstruction (MC-MRRec) Challenge had two primary goals: 1) compare different MR image reconstruction models on a large dataset and 2) assess the generalizability of these models to datasets acquired with a different number of receiver coils (i.e., multiple channels). The challenge had two tracks: Track 0...

Background: Adults with significant childhood trauma and/or serious mental illness may exhibit persistent structural brain changes within limbic structures, including the amygdala. Little is known about the structure of the amygdala prior to the onset of SMI, despite the relatively high prevalence of trauma in at-risk youth. Methods: Data were g...

Deep learning models have shown potential for reconstructing undersampled, multi-channel magnetic resonance (MR) image acquisitions. Recently proposed methods, however, have not leveraged information from prior subject-specific MR imaging sessions. Such data are often readily available through a picture archiving and communication system (PACS). We...

The U-net is a deep-learning network model that has been used to solve a number of inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net, operating in k-space (K) and image (I) domains, were evaluated for multi-channel magnetic resonance (MR) image reconstruction. The two-element network combinations were evaluat...

We propose a dual-domain cascade of U-nets (i.e. a "W-net") operating in both the spatial frequency and image domains to enhance low-dose CT (LDCT) images without the need for proprietary x-ray projection data. The central slice theorem motivated the use of the spatial frequency domain in place of the raw sinogram. Data were obtained from the AAPM...

Aim: Alterations in limbic structures may be present before the onset of serious mental illness (SMI), but whether sub-field specific limbic brain changes parallel stages in clinical risk is unknown. To address this gap, we compared hippocampus, amygdala and thalamus subfield-specific volumes in adolescents at various stages of risk for mental ill...

Lossy image compression allows for efficient storage and transfer of image data with varying degrees of image degradation. However, lossy compression is not commonly used in medical imaging as the process may irreversibly remove information that defines clinically important image features. The lossy component of JPEG compression is represented as l...

The U-net is a deep-learning network model that has been used to solve a number of inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net, operating in k-space (K) and image (I) domains, were evaluated for multi-channel magnetic resonance (MR) image reconstruction. The two element network combinations were evaluat...

Finding a clinically useful neuroimaging biomarker that can predict treatment response in patients with major depressive disorder (MDD) is challenging, in part because of poor reproducibility and generalizability of findings across studies. Previous work has suggested that posterior hippocampal volumes in depressed patients may be associated with a...

Manual annotation is considered to be the "gold standard" in medical imaging analysis. However, medical imaging datasets that include expert manual segmentation are scarce as this step is time-consuming, and therefore expensive. Moreover, single-rater manual annotation is most often used in data-driven approaches making the network biased to only t...

Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance brain images. The cohort includes 190 subjects with CA, white mater hyperintensites of presumed vasc...

Background: Hippocampal volume (HV) alterations can be present before the onset of psychosis in those individuals who are at clinical risk for psychosis with subthreshold symptomatology. However, it is unclear whether sub-field specific HV changes parallel the progression of SMI from the premorbid through the distress and attenuated syndromes to...

Background: Recent research has explored hippocampal subregional volumes (HSV) in Major Depressive Disorder (MDD). Here we examined the effects of gender and age on HSV in MDD. Methods: Data from the multi-site CAN-BIND program was obtained and included 191 subjects with MDD (females = 126) and 165 controls (HC; females = 99). Freesurfer 6.0 su...

Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation methods that are both fast and reliable over time. Segmentation algorithms that employ deep convoluti...

Atherosclerosis is one of the main causes of stroke and is responsible for millions of deaths every year. Magnetic resonance (MR) is a common way of assessing carotid artery atherosclerosis. Cine fast spin echo (FSE) imaging is a new MR method that can now obtain dynamic image data of the carotid artery across the cardiac cycle. This work introduce...

Deep-learning-based magnetic resonance (MR) imaging reconstruction techniques have the potential to accelerate MR image acquisition by reconstructing in real-time clinical quality images from k-spaces sampled at rates lower than specified by the Nyquist-Shannon sampling theorem, which is known as compressed sensing. In the past few years, several d...

Decreasing magnetic resonance (MR) image acquisition times can potentially reduce procedural cost and make MR examinations more accessible. Compressed sensing (CS)-based image reconstruction methods, for example, decrease MR acquisition time by reconstructing high-quality images from data that were originally sampled at rates inferior to the Nyquis...

In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in terms of spectral and spatial resolution, which makes the data sets they produce a valuable source for land cover classification. The availability of hyperspectral data with fine spatial resolution has revolutionized hyperspectral image (HSI) classification tech...

[This is a preprint, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore. ] Airborne and spaceborne hyperspectral imaging systems have advanced in recent years in terms of spectral and spatial resolution, which makes data sets produced by them a valuable source for land-cover classification. The availa...

A better understanding of normal human brain aging is required to better study age-related neurodegeneration including cognitive impairment. We propose an automatic deep-learning method to analyze the predictive ability of magnetic resonance images with respect to age, sex and the presence of an age-related pathology (white matter hyperintensity, W...

The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the "gold-standard". However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. We propose in this work the use of wh...

Convolutional neural networks (CNN) for medical imaging are constrained by the number of annotated data required in the training stage. Usually, manual annotation is considered to be the "gold standard". However, medical imaging datasets that include expert manual segmentation are scarce as this step is time-consuming, and therefore expensive. More...

This Grand Challenge at MICCAI 2017 aims to directly compare methods for the automatic segmentation of White Matter Hyperintensities (WMH) of presumed vascular origin. Our method automatically segment WMH by using texture-based classification of pixels within the brain white matter. It uses no a priori information about the WMH size, contrast or lo...

This is a preprint, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.

The iamxt is an array-based max-tree toolbox implemented in Python using the NumPy library for array processing. It has state of the art methods for building and processing the max-tree, and a large set of visualization tools that allow to view the tree and the contents of its nodes. The array-based programming style and max-tree representation use...

This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. CC-3...

Face recognition systems are gaining momentum with current developments in computer vision. At the same time, tactics to mislead these systems are getting more complex, and counter-measure approaches are necessary. Following the current progress with convolutional neural networks (CNN) in classification tasks, we present an approach based on transf...

Face recognition systems are gaining momentum with current developments in computer vision. At the same time, tactics to mislead these systems are getting more complex, and counter-measure approaches are necessary. Following the current progress with convolutional neural networks (CNN) in classification tasks, we present an approach based on transf...