- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models.

Segmentation of brain structures on magnetic resonance imaging (MRI) is a highly relevant neuroimaging topic, as it is a prerequisite for different analyses such as volumetry or shape analysis. Automated segmentation facilitates the study of brain structures in larger cohorts when compared with manual segmentation, which is time-consuming. However, the development of most automated methods relies on large and manually annotated datasets, which limits the generalizability of these methods.

Spectro-ViT: A vision transformer model for GABA-edited MEGA-PRESS reconstruction using spectrograms.

This study investigated the use of a Vision Transformer (ViT) for reconstructing GABA-edited Magnetic Resonance Spectroscopy (MRS) data from a reduced number of transients. Transients refer to the samples collected during an MRS acquisition by repeating the experiment to generate a signal of sufficient quality. Specifically, 80 transients were used instead of the typical 320 transients, aiming to reduce scan time.

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.

Purpose: 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.

Methods: There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance.

Machine and Deep Learning Dominate Recent Innovations in Sensors, Signals and Imaging Informatics.

Objectives: This review presents research papers highlighting notable developments and trends in sensors, signals, and imaging informatics (SSII) in 2022.

Method: We performed a bibliographic search in PubMed combining Medical Subject Heading (MeSH) terms and keywords to create particular queries for sensors, signals, and imaging informatics. Only papers published in journals containing greater than three articles in the search query were considered.

Microstructural Changes in the Corpus Callosum in Systemic Lupus Erythematous.

Central nervous system (CNS) involvement in childhood-onset systemic lupus erythematosus (cSLE) occurs in more than 50% of patients. Structural magnetic resonance imaging (MRI) has identified global cerebral atrophy, as well as the involvement of the corpus callosum and hippocampus, which is associated with cognitive impairment. In this cross-sectional study we included 71 cSLE (mean age 24.

Reconsidering the olfactory and brain structures in Kallmann's syndrome: New findings in the analysis of volumetry.

Objective: Kallmann's syndrome (KS) is characterized by hypogonadotropic hypogonadism and olfactory disorders. The complementary exams for evaluating of patients with hypogonadotrophic hypogonadism are important for the diagnosis and management of these patients.

Patients: We performed a well-established olfactory Sniffin' Stick test (SST) on 17 adult patients with KS and brain magnetic resonance imaging (MRI) to evaluate olfactory structures and further analysis by Freesurfer, a software for segmentation and volumetric evaluation of brain structures.

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images.

Objectives: Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods.

A benchmark for hypothalamus segmentation on T1-weighted MR images.

The hypothalamus is a small brain structure that plays essential roles in sleep regulation, body temperature control, and metabolic homeostasis. Hypothalamic structural abnormalities have been reported in neuropsychiatric disorders, such as schizophrenia, amyotrophic lateral sclerosis, and Alzheimer's disease. Although mag- netic resonance (MR) imaging is the standard examination method for evaluating this region, hypothalamic morphological landmarks are unclear, leading to subjec- tivity and high variability during manual segmentation.

Multi-Coil MRI Reconstruction Challenge-Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations.

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 presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans.

Neuropsychiatric manifestations in childhood-onset systemic lupus erythematosus.

Neuropsychiatric manifestations occur frequently and are challenging to diagnose in childhood-onset systemic lupus erythematosus (SLE). Most patients with childhood-onset SLE have neuropsychiatric events in the first 2 years of disease. 30-70% of patients present with more than one neuropsychiatric event during their disease course, with an average of 2-3 events per person.

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets.

Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed "batch effects") is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool.

Rapidly deploying a COVID-19 decision support system in one of the largest Brazilian hospitals.

The COVID-19 pandemic generated research interest in automated models to perform classification and segmentation from medical imaging of COVID-19 patients, However, applications in real-world scenarios are still needed. We describe the development and deployment of COVID-19 decision support and segmentation system. A partnership with a Brazilian radiologist consortium, gave us access to 1000s of labeled computed tomography (CT) and X-ray images from São Paulo Hospitals.

Axonal dysfunction is associated with interferon-γ levels in childhood-onset systemic lupus erythematosus: a multivoxel magnetic resonance spectroscopy study.

Objective: Axonal/neuronal damage has been shown to be a pathological finding that precedes neuropsychiatric manifestations in SLE. The objective of this study was to determine the presence of axonal dysfunction in childhood-onset SLE patients (cSLE) and to determine clinical, immunological and treatment features associated with its occurrence.

Methods: We included 86 consecutive cSLE patients [median age 17 (range 5-28) years] and 71 controls [median age 18 (5-28) years].

Automatic MR image quality evaluation using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging.

Motion artifacts on magnetic resonance (MR) images degrade image quality and thus negatively affect clinical and research scanning. Considering the difficulty in preventing patient motion during MR examinations, the identification of motion artifact has attracted significant attention from researchers. We propose an automatic method for the evaluation of motion corrupted images using a deep convolutional neural network (CNN).

Hippocampus segmentation on epilepsy and Alzheimer's disease studies with multiple convolutional neural networks.

Hippocampus segmentation on magnetic resonance imaging is of key importance for the diagnosis, treatment decision and investigation of neuropsychiatric disorders. Automatic segmentation is an active research field, with many recent models using deep learning. Most current state-of-the art hippocampus segmentation methods train their methods on healthy or Alzheimer's disease patients from public datasets.

Proton magnetic resonance spectroscopy (H-MRS) in rheumatic autoimmune diseases: A systematic review.

Background/purpose: Proton magnetic resonance spectroscopy (H-MRS) has been shown to be an important non-invasive tool to quantify neuronal loss or damage in the investigation of central nervous system (CNS) disorders. The purpose of this article is to discuss the clinical utility of H-MRS in determining CNS involvement in individuals with rheumatic autoimmune diseases.

Methods: This study is a systematic review of the literature, conducted during the month of November and December of 2019 of articles published in the last 16 years (2003-2019).

A framework for quality control of corpus callosum segmentation in large-scale studies.

Background: The corpus callosum (CC) is the largest white matter structure in the brain, responsible for the interconnection of the brain hemispheres. Its segmentation is a required preliminary step for any posterior analysis, such as parcellation, registration, and feature extraction. In this context, the quality control (QC) of CC segmentation allows studies on large datasets with no human interaction, and the proper usage of available automated and semi-automated algorithms.

Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks.

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 that single expert.

Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set.

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 vascular origin or multiple sclerosis, as well as 211 presumed healthy subjects.

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

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. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking.

Olfactory function in systemic lupus erythematosus and systemic sclerosis. A longitudinal study and review of the literature.

Background/purpose: To evaluate olfactory function in systemic lupus erythematosus (SLE), systemic sclerosis (SSc) and healthy controls over a 2-year period, and to determine the association of olfactory dysfunction with age, disease activity, disease damage, treatment, anxiety and depression symptoms and limbic structures volumes.

Methods: Consecutive SLE and SSc patients were enrolled in this study. Clinical, laboratory disease activity and damage were assessed according to diseases specific guidelines.

Brain diffusion tensor MRI in systematic lupus erythematosus: A systematic review.

Diffusion tensor imaging (DTI) maps the brain's microstructure by measuring fractional anisotropy (FA) and mean diffusivity (MD). This systematic review describes brain diffusion tensor Magnetic resonance imaging (MRI) studies in systemic lupus erythematosus (SLE).The literature was reviewed following the PRISMA guidelines and using the terms "lupus", "systemic lupus erythematosus", "SLE", "diffusion tensor imaging", "DTI", "white matter" (WM), "microstructural damage", "tractography", and "fractional anisotropy"; the search included articles published in English from January 2007 to April 2017.

An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement.

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.

Prevalence and features of metabolic syndrome in childhood-onset systemic lupus erythematosus.

To estimate the prevalence and features of metabolic syndrome (MetS) in childhood-onset systemic lupus erythematosus (cSLE), we performed a cross-sectional study of 76 consecutive cSLE patients and 54 healthy controls, age and sex matched. All individuals were assessed for anthropometric and MetS features according to World Health Organization (WHO), NCEP Adult Treatment Panel III (NCEP-ATP III), and International Diabetes Federation (IDF) criteria. The cSLE patients were further assessed for clinical and laboratory manifestations, disease activity (Systemic Lupus Erythematosus Disease Activity Index), cumulative damage (Systemic Lupus International Collaborating Clinics (SLICC)), and current and cumulative drug exposures.