4 citations · 8 across the 5 of their papers we have counts for
5 papers
Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas
José V. Manjón, Sergio Morell-Ortega, Marina Ruiz-Perez +16
In this paper, we introduce a novel structural holistic Atlas (holiAtlas) of the human brain anatomy based on multimodal and high-resolution MRI that covers several anatomical leve…
DeepCERES: A Deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI
Sergio Morell-Ortega, Marina Ruiz-Perez, Marien Gadea +7
This paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution ($1 \text{…
DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI
Marina Ruiz-Perez, Sergio Morell-Ortega, Marien Gadea +7
The implication of the thalamus in multiple neurological pathologies makes it a structure of interest for volumetric analysis. In the present work, we have designed and implemented…
Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Review
Harold Brayan Arteaga-Arteaga, Melissa delaPava, Alejandro Mora-Rubio +12
There is a necessity to develop affordable, and reliable diagnostic tools, which allow containing the COVID-19 spreading. Machine Learning (ML) algorithms have been proposed to des…
Automatic Semantic Segmentation of the Lumbar Spine: Clinical Applicability in a Multi-parametric and Multi-centre Study on Magnetic Resonance Images
Jhon Jairo Saenz-Gamboa, Julio Domenech, Antonio Alonso-Manjarrés +2
One of the major difficulties in medical image segmentation is the high variability of these images, which is caused by their origin (multi-centre), the acquisition protocols (mult…