309 citations · 450 across the 14 of their papers we have counts for
14 papers
High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models
Livia Rodrigues, Martina Bocchetta, Oula Puonti +6
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 sh…
H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI for hypothalamus subregion segmentation
Livia Rodrigues, Martina Bocchetta, Oula Puonti +6
The hypothalamus is a small structure located in the center of the brain and is involved in significant functions such as sleeping, temperature, and appetite control. Various neuro…
Spectro-ViT: A Vision Transformer Model for GABA-edited MRS Reconstruction Using Spectrograms
Gabriel Dias, Rodrigo Pommot Berto, Mateus Oliveira +7
Purpose: To investigate the use of a Vision Transformer (ViT) to reconstruct/denoise GABA-edited magnetic resonance spectroscopy (MRS) from a quarter of the typically acquired numb…
Automatic segmentation of lung findings in CT and application to Long COVID
Diedre S. Carmo, Rosarie A. Tudas, Alejandro P. Comellas +4
Automated segmentation of lung abnormalities in computed tomography is an important step for diagnosing and characterizing lung disease. In this work, we improve upon a previous me…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide
Diedre Carmo, Gustavo Pinheiro, Lívia Rodrigues +3
Medical image segmentation is an increasingly popular area of research in medical imaging processing and analysis. However, many researchers who are new to the field struggle with…
Open-source tool for Airway Segmentation in Computed Tomography using 2.5D Modified EfficientDet: Contribution to the ATM22 Challenge
Diedre Carmo, Leticia Rittner, Roberto Lotufo
Airway segmentation in computed tomography images can be used to analyze pulmonary diseases, however, manual segmentation is labor intensive and relies on expert knowledge. This ma…
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations
Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos +20
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific communi…
Hippocampus Segmentation on Epilepsy and Alzheimer's Disease Studies with Multiple Convolutional Neural Networks
Diedre Carmo, Bruna Silva, Clarissa Yasuda +2
Hippocampus segmentation on magnetic resonance imaging is of key importance for the diagnosis, treatment decision and investigation of neuropsychiatric disorders. Automatic segment…
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser +41
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are o…
Extended 2D Consensus Hippocampus Segmentation
Diedre Carmo, Bruna Silva, Clarissa Yasuda +2
Hippocampus segmentation plays a key role in diagnosing various brain disorders such as Alzheimer's disease, epilepsy, multiple sclerosis, cancer, depression and others. Nowadays,…
Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks
Oeslle Lucena, Roberto Souza, Leticia Rittner +2
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…
Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping
Oeslle Lucena, Roberto Souza, Letícia Rittner +2
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-s…