Mario Ceresa

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.

Monopolar Stimulation of the Implanted Cochlea: A Synthetic Population-Based Study

Abstract: Cochlear implantation is carried out to recover the sense of hearing. However, its functional outcome varies highly between patients. In the current work, we present a study to assess the functional outcomes of cochlear implants considering the inter-variability found among a population of patients. In order to capture the cochlear anatomical details, a statistical shape model is created from high-resolution human \(\mu \)CT data. A population of virtual patients is automatically generated by sampling new anatomical instances from the statistical shape model. For each virtual patient, an implant insertion is simulated and a finite element model is generated to estimate the electrical field created into the cochlea. These simulations are defined according to the monopolar stimulation protocol of a cochlear implant and a prediction of the voltage spread over the population of virtual patients is evaluated.

Multi-region statistical shape model for cochlear implantation

Abstract: Statistical shape models are commonly used to analyze the variability between similar anatomical structures and their use is established as a tool for analysis and segmentation of medical images. However, using a global model to capture the variability of complex structures is not enough to achieve the best results. The complexity of a proper global model increases even more when the amount of data available is limited to a small number of datasets. Typically, the anatomical variability between structures is associated to the variability of their physiological regions. In this paper, a complete pipeline is proposed for building a multi-region statistical shape model to study the entire variability from locally identified physiological regions of the inner ear. The proposed model, which is based on an extension of the Point Distribution Model (PDM), is built for a training set of 17 high-resolution images (24.5 μm voxels) of the inner ear. The model is evaluated according to its generalization ability and specificity. The results are compared with the ones of a global model built directly using the standard PDM approach. The evaluation results suggest that better accuracy can be achieved using a regional modeling of the inner ear.

Finite element model for patient-specific functional simulations of cochlear implants

Abstract: We present an innovative image analyisis pipeline to perform patient-speci c biomechanical and functional simulations of the inner human ear. A high-resolution, cadaveric, mCT volumetric image portraying the detailed geometry of the cochlea is converted into a mesh in order to build a Finite Element Method (FEM). The constitutive model for the FEM is based on a Navier-Stokes formulation for compressible Newtonian uid, coupled with an elastic solid model. The simulation includes uid-structure interactions. Further to this, the FEM mesh is deformed to a patient-speci c low-resolution Cone Beam CT (CBCT) dataset to propagate functional information to the speci c anatomy of the patient. Illustrative results of how the FE-model responds to various acoustic stimuli are shown by analyzing the tonotopic mapping of the cochlear membrane vibration.

Hierarchical Shape Modeling of the Cochlea and Surrounding Risk Structures for Minimally Invasive Cochlear Implant Surgery

Abstract: Knowing the anatomical shape and position of structures surrounding the cochlea is essential in planning minimally invasive cochlear implant surgery. In this work, a Multiobject Hierarchical Statistical Shape Model (MO-SSM) based of wavelet decomposition is created from clinical cone-beam CT datasets of the inner, middle and outer auditory system and surrounding structures. The methodology incorporates an algorithm that automatically segregates structures as the level of detail is increased, leading to a global description of the whole surgical site at the lowest resolution and detailed anatomic models at the highest resolution. This model is the basis for the automatic segmentation of patient data, allowing to quantify the relative position of risk structures in planning the intervention.

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans

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.

Extraction of Airways From CT (EXACT'09)

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.