141 citations · 249 across the 12 of their papers we have counts for
12 papers
Pathological MRI Segmentation by Synthetic Pathological Data Generation in Fetuses and Neonates
Misha P. T Kaandorp, Damola Agbelese, Hosna Asma-ull +6
Developing new methods for the automated analysis of clinical fetal and neonatal MRI data is limited by the scarcity of annotated pathological datasets and privacy concerns that of…
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Kelly Payette, Céline Steger, Roxane Licandro +64
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and th…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Synthesis of realistic fetal MRI with conditional Generative Adversarial Networks
Marina Fernandez Garcia, Rodrigo Gonzalez Laiz, Hui Ji +2
Fetal brain magnetic resonance imaging serves as an emerging modality for prenatal counseling and diagnosis in disorders affecting the brain. Machine learning based segmentation pl…
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Kelly Payette, Hongwei Li, Priscille de Dumast +55
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital ste…
Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation
Priscille de Dumast, Hamza Kebiri, Kelly Payette +3
The quantitative assessment of the developing human brain in utero is crucial to fully understand neurodevelopment. Thus, automated multi-tissue fetal brain segmentation algorithms…
FaBiAN: A Fetal Brain magnetic resonance Acquisition Numerical phantom
Hélène Lajous, Christopher W. Roy, Tom Hilbert +16
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health lat…
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
Kelly Payette, Priscille de Dumast, Hamza Kebiri +17
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.…
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels
Kelly Payette, Raimund Kottke, Andras Jakab
Segmentation of the developing fetal brain is an important step in quantitative analyses. However, manual segmentation is a very time-consuming task which is prone to error and mus…
Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair
Kelly Payette, Ueli Moehrlen, Luca Mazzone +5
Open spina bifida (SB) is one of the most common congenital defects and can lead to impaired brain development. Emerging fetal surgery methods have shown considerable success in th…
Postoperative brain volumes are associated with one-year neurodevelopmental outcome in children with severe congenital heart disease
Eliane Meuwly, Maria Feldmann, Walter Knirsch +8
Children with congenital heart disease (CHD) remain at risk for neurodevelopmental impairment despite improved peri- and intraoperative care. Our prospective cohort study aimed to…