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Author

Mario Ceresa

Other affiliations: University of Navarra
Bio: Mario Ceresa is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: Cochlear implant & Medicine. The author has an hindex of 13, co-authored 55 publications receiving 502 citations. Previous affiliations of Mario Ceresa include University of Navarra.


Papers
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Journal ArticleDOI
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.

114 citations

Journal ArticleDOI
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.

70 citations

Journal ArticleDOI
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.

52 citations

Journal ArticleDOI
TL;DR: Micro-CT-derived descriptors are more sensitive than the other methods compared, to detect in vivo early signs of the disease.
Abstract: To define the sensitivity of microcomputed tomography- (micro-CT-) derived descriptors for the quantification of lung damage caused by elastase instillation. Materials and Methods. The lungs of 30 elastase treated and 30 control A/J mice were analyzed 1, 6, 12, and 24 hours and 7 and 17 days after elastase instillation using (i) breath-hold-gated micro-CT, (ii) pulmonary function tests (PFTs), (iii) RT-PCR for RNA cytokine expression, and (iv) histomorphometry. For the latter, an automatic, parallel software toolset was implemented that computes the airspace enlargement descriptors: mean linear intercept (Lm) and weighted means of airspace diameters (D0, D1, and D2). A Support Vector Classifier was trained and tested based on three nonhistological descriptors using D2 as ground truth. Results. D2 detected statistically significant differences (P < 0.01) between the groups at all time points. Furthermore, D2 at 1 hour (24 hours) was significantly lower (P < 0.01) than D2 at 24 hours (7 days). The classifier trained on the micro-CT-derived descriptors achieves an area under the curve (AUC) of 0.95 well above the others (PFTS AUC = 0.71; cytokine AUC = 0.88). Conclusion. Micro-CT-derived descriptors are more sensitive than the other methods compared, to detect in vivo early signs of the disease.

51 citations

Journal ArticleDOI
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.
Abstract: Objectives To define the potential, limitations and synergies of micro-CT and other non-radiological techniques for the quantification of emphysema and related processes in mice, by performing a complete characterization of the elastase-induced emphysema model.

41 citations


Cited by
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Journal ArticleDOI
TL;DR: Medical imaging systems: Physical principles and image reconstruction algorithms for magnetic resonance tomography, ultrasound and computer tomography (CT), and applications: Image enhancement, image registration, functional magnetic resonance imaging (fMRI).

536 citations

Posted ContentDOI
22 Apr 2020-medRxiv
TL;DR: A novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT scans and outperforms most cutting-edge segmentation models and advances the state-of-the-art technology.
Abstract: Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

354 citations

01 Jan 2010

301 citations

Journal ArticleDOI
TL;DR: A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.
Abstract: This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate 15 different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of 20 chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.

241 citations