Mariano Cabezas

Mariano Cabezas
VHIR Vall d’Hebron Research Institute | VHIR · Magnetic Resonance and Neuroradiology Research Group

Computer Science

About

44
Publications
25,201
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2,821
Citations
Additional affiliations
September 2010 - July 2013
Universitat de Girona
Position
  • PhD Student

Publications

Publications (44)
Preprint
Full-text available
Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors h...
Article
Full-text available
Brain atrophy quantification plays a fundamental role in neuroinformatics since it permits studying brain development and neurological disorders. However, the lack of a ground truth prevents testing the accuracy of longitudinal atrophy quantification methods. We propose a deep learning framework to generate longitudinal datasets by deforming T1-w b...
Article
Full-text available
Autism Spectrum Disorder (ASD) is a brain disorder that is typically characterized by deficits in social communication and interaction, as well as restrictive and repetitive behaviors and interests. During the last years, there has been an increase in the use of magnetic resonance imaging (MRI) to help in the detection of common patterns in autism...
Article
Full-text available
Introduction: Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w lesions on brain MR scans is considered a predictive biomarker for the disease. In this study, we propose a fully convolutional neural network (FCNN) to detect new T2-w lesions...
Article
Full-text available
Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) has attracted the attention of medical doctors and researchers since variations in tissue volume and shape permit diagnosing and monitoring neurological diseases. Several proposals have been designed throughout the years comprising conventional machine learning strategies as wel...
Article
Full-text available
In recent years, some convolutional neural networks (CNNs) have been proposed to segment sub-cortical brain structures from magnetic resonance images (MRIs). Although these methods provide accurate segmentation, there is a reproducibility issue regarding segmenting MRI volumes from different image domains – e.g., differences in protocol, scanner, a...
Preprint
Full-text available
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions...
Article
Full-text available
Our research [Team Name: URAN] of 'Neuromorphic Neural Network for Multimodal Brain Tumor Segmentation and Survival analysis' demonstrates its performance on 'Overall Survival Prediction', without any prior training using multi-modal MRI Image segmentation by medical doctors (though provided by BraTS 2018 challenge data). Two segmentation categorie...
Article
Magnetic resonance imaging (MRI) synthesis has attracted attention due to its various applications in the medical imaging domain. In this paper, we propose generating synthetic multiple sclerosis (MS) lesions on MRI images with the final aim to improve the performance of supervised machine learning algorithms, therefore avoiding the problem of the...
Preprint
Full-text available
In this paper, we propose generating synthetic multiple sclerosis (MS) lesions on MRI images with the final aim to improve the performance of supervised machine learning algorithms, therefore avoiding the problem of the lack of available ground truth. We propose a two-input two-output fully convolutional neural network model for MS lesion synthesis...
Article
Full-text available
In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evalu...
Preprint
Full-text available
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions...
Preprint
Full-text available
Segmenting tumors and their subregions is a challenging task as demonstrated by the annual BraTS challenge. Moreover, predicting the survival of the patient using mainly imaging features, while being a desirable outcome to evaluate the treatment of the patient, it is also a difficult task. In this paper, we present a cascaded pipeline to segment th...
Article
Full-text available
We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infra...
Preprint
Full-text available
We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infra...
Article
Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time as morphological changes in these structures are related to different neurodegenerative disorders. However, manual segmentation of these structures can be tedious and prone to variability, highlighting th...
Preprint
Full-text available
In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evalu...
Preprint
Full-text available
Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) has attracted the attention of medical doctors and researchers since variations in tissue volume help in diagnosing and monitoring neurological diseases. Several proposals have been designed throughout the years comprising conventional machine learning strategies as well as conv...
Article
Full-text available
Introduction Longitudinal magnetic resonance imaging (MRI) analysis has an important role in multiple sclerosis diagnosis and follow-up. The presence of new T2-w lesions on brain MRI scans is considered a prognostic and predictive biomarker for the disease. In this study, we propose a supervised approach for detecting new T2-w lesions using feature...
Poster
Full-text available
We present here a supervised approach for detecting newly appearing MS lesions that combines both subtraction and deformation field features. Specifically, we use a logistic regression classifier trained with features from the baseline and follow-up intensities, subtraction values, and deformation field operators to provide a final segmentation.
Preprint
Full-text available
Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time because morphological changes in these structures are related to different neurodegenerative disorders. However, manual segmentation of these structures can be tedious and prone to variability, highlighti...
Data
Volume differences in mm3 (generated patients – healthy control) for the 100 generated images using three publicly available software. The green boxplots show the differences when there were MS lesions generated on the brain but not on top of that particular structure. On the other hand, red boxplots stand for lesioned structures. Acronyms from lef...
Article
Full-text available
In recent years, many automatic brain structure segmentation methods have been proposed. However, these methods are commonly tested with non-lesioned brains and the effect of lesions on their performance has not been evaluated. Here, we analyze the effect of multiple sclerosis (MS) lesions on three well-known automatic brain structure segmentation...
Article
In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is...
Article
Background and purpose: Detection of disease activity, defined as new/enlarging T2 lesions on brain MR imaging, has been proposed as a biomarker in MS. However, detection of new/enlarging T2 lesions can be hindered by several factors that can be overcome with image subtraction. The purpose of this study was to improve automated detection of new T2...
Conference Paper
Purpose: Magnetic resonance imaging is nowadays the hallmark to diagnose multiple sclerosis (MS), characterized by white matter lesions. Several approaches have been recently presented to tackle the lesion segmentation problem, but none of them have been accepted as a standard tool in the daily clinical practice. In this work we present yet another...
Article
Full-text available
Lesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra- and inter-rater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images. Our approach is based o...
Article
Full-text available
The accuracy of automatic tissue segmentation methods can be affected by the presence of hypointense white matter lesions during the tissue segmentation process. Our aim was to evaluate the impact of MS white matter lesions on the brain tissue measurements of 6 well-known segmentation techniques. These include straightforward techniques such as Art...
Article
Ground-truth annotations from the well-known Internet Brain Segmentation Repository (IBSR) datasets consider Sulcal cerebrospinal fluid (SCSF) voxels as gray matter. This can lead to bias when evaluating the performance of tissue segmentation methods. In this work we compare the accuracy of 10 brain tissue segmentation methods analyzing the effects...
Article
Magnetic resonance imaging (MRI) is frequently used to detect and segment multiple sclerosis lesions due to the detailed and rich information provided. We present a modified expectation-maximisation algorithm to segment brain tissues (white matter, grey matter, and cerebro-spinal fluid) as well as a partial volume class containing fluid and grey ma...
Article
Full-text available
Registration is a key step in many automatic brain Magnetic Resonance Imaging (MRI) applications. In this work we focus on longitudinal registration of brain MRI for Multiple Sclerosis (MS) patients. First of all, we analyze the effect that MS lesions have on registration by synthetically eliminating some of the lesions. Our results show how a wide...
Conference Paper
Automatic multiple sclerosis (MS) lesion segmentation in magnetic resonance imaging (MRI) is a challenging task due to the small size of the lesions, its heterogeneous shape and distribution, overlapping tissue intensity distributions, and the inherent artifacts of MRI. In this paper we propose a pipeline for MS lesion segmentation that combines pr...
Article
Brain extraction, also known as skull stripping, is one of the most important preprocessing steps for many automatic brain image analysis. In this paper we present a new approach called Multispectral Adaptive Region Growing Algorithm (MARGA) to perform the skull stripping process. MARGA is based on a region growing (RG) algorithm which uses the com...
Article
Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentatio...
Article
Full-text available
Breast ultrasound (BUS) imaging is an imaging modality used for the detection and diagnosis of breast lesions and it has become a crucial modality nowadays specially for providing a complementary view when other modalities (i.e. mammography) are not conclusive. However, lesion detection in ultrasound images is still a challenging problem due to the...
Article
Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as...

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Projects

Project (1)
Archived project
- Understand how tissue segmentation has been addressed previously - Understand strengths and weaknesses of current methods