M
Mario Ceresa
Researcher at Pompeu Fabra University
Publications - 61
Citations - 737
Mario Ceresa is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: Cochlear implant & Computer science. The author has an hindex of 13, co-authored 55 publications receiving 502 citations. Previous affiliations of Mario Ceresa include University of Navarra.
Papers
More filters
Journal ArticleDOI
Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks.
Karen López-Linares,Nerea Aranjuelo,Luis Kabongo,Gregory Maclair,Nerea Lete,Mario Ceresa,Ainhoa García-Familiar,Iván Macía,Miguel Ángel González Ballester +8 more
TL;DR: A new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducibleThrombus region of interest detection and subsequent fine thrombus segmentation and a new segmentation network architecture, based on Fully convolutional Networks and a Holistically‐Nested Edge Detection Network, is presented.
Journal ArticleDOI
Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects.
Jordina Torrents-Barrena,Gemma Piella,Narcís Masoller,Eduard Gratacós,Elisenda Eixarch,Mario Ceresa,Miguel Ángel González Ballester +6 more
TL;DR: This review covers state‐of‐the‐art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time.
Journal ArticleDOI
Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline
Ilaria Bonavita,Xavier Rafael-Palou,Mario Ceresa,Gemma Piella,Vicent J. Ribas,Miguel Ángel González Ballester +5 more
TL;DR: This article proposes to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection by integrating predictive models of nodulemalignancy into a limited size lung cancer datasets.
Journal ArticleDOI
Quantification of lung damage in an elastase-induced mouse model of emphysema
Arrate Muñoz-Barrutia,Mario Ceresa,Xabier Artaechevarria,Luis M. Montuenga,Carlos Ortiz-de-Solorzano +4 more
TL;DR: Micro-CT-derived descriptors are more sensitive than the other methods compared, to detect in vivo early signs of the disease.
Journal ArticleDOI
Evaluation of micro-CT for emphysema assessment in mice: comparison with non-radiological techniques
Xabier Artaechevarria,David Blanco,Gabriel de Biurrun,Mario Ceresa,Daniel Pérez-Martín,Gorka Bastarrika,Juan P. de Torres,Javier J. Zulueta,Luis M. Montuenga,Carlos Ortiz-de-Solorzano,Arrate Muñoz-Barrutia +10 more
TL;DR: Histomorphometry is the most sensitive technique since it detects airspace enlargement before the other methods (1 h after treatment) and micro-CT correlates well with histology (r2 = 0.63) proving appropriate for longitudinal studies.