activity
20172022
most citedStandardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

309 citations · 312 across the 3 of their papers we have counts for

collaborators

6 papers

eess.IV20223 cited

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…

eess.IV2020

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…

cs.CV2019309 cited

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…

eess.IV2019

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,…

cs.CV2018

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…

eess.IV2017

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…