Meritxell Bach Cuadra

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We propose an algorithm for very high-resolution satellite image classification that combines non-supervised segmentation with a supervised classification. Both multi-spectral data and local spatial priors are used in the Gaussian Hidden Markov Random Field (GHMRF) model for the segmentation. Then, two classifiers, Mahalanobis distance classifier and SVM, are studied using intensity, texture and shape features. Validation is done qualitatively and quantitatively by comparison with a manual classification us

We present an open-source ITK implementation of a direct Fourier method for tomographic reconstruction, applicable to parallel-beam x-ray images. Direct Fourier reconstruction makes use of the central-slice theorem to build a polar 2D Fourier space from th ...

In this paper, we present the segmentation of the head and neck lymph node regions using a new active contour based atlas registration model. We propose to segment the lymph node regions without directly including them in the atlas registration process; in ...

The high complexity of cortical convolutions in humans remains a mystery for both engineers to measure and compare it, and for biologists to understand it. We developed a method for the quantification of the cortical area allowed by the process of folding. Our method uses accurate three-dimensional cortical reconstruction and compute local measurements of gyrification at thousands of points over the whole cortical surface. The potential of our method to identify and localize subregional gyrification abnorma

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer’s disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and e ...

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease pat ...

This document presents a brief description of the Medical Image Processing (MIP) library developed by the Brain Group of the Signal Processing Institute (ITS) at Swiss Federal Institute of Technology (EPFL). This library is a result of more than three years of research by Meritxell Bach Cuadra, Torsten Butz, Eduardo Solanas and Olivier Cuisenare, carried out under the supervision of Jean-Philippe Thiran. You can find more detailed information about the library at our web page located at http://ltswww.epfl.c

The use of 3D Magnetic Resonance Imaging (MRI) has attracted growing attention for the purpose of diagnosis and treatment planning of intraocular ocular cancers. Precise segmentation of such tumors are highly important to characterize tumors, their progres ...

In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatment of Parkinson s disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different regi

This paper presents an ITK implementation for exporting the contours of the automated segmentation results to DICOM-RT Structure Set format. The “radiotherapy structure set” (RTSTRUCT) object of the DICOM standard is used for the transfer of patient structures and related data, between the devices found within and outside the radiotherapy department. It mainly contains the information of regions of interest (ROIs) and points of interest (E.g. dose reference points). In many cases, rather than manually drawi

Accurate characterization of in utero human brain maturation is critical as it involves complex interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool complementary to the u ...

Validation is the main bottleneck preventing the adoption of many medical image processing algorithms in the clinical practice. In the classical approach, a-posteriori analysis is performed based on some objective metrics. In this work, a different approac ...

This paper presents 3-D brain tissue classification schemes using three recent promising energy minimization methods for Markov random fields: graph cuts, loopy belief propagation and tree-reweighted message passing. The classification is performed using t ...

We propose a method for brain atlas deformation in presence of large space-occupying tumors or lesions, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its central point. Atlas-based methods have been of limited use for segmenting brains that have been drastically altered by the presence of large space-occupying lesions. Our approach involves four steps. First, an affine registration brings the atlas and the patient into global correspondence. Secondly, a local r

We propose a method for brain atlas deformation in presence of large space-occupying tumors or lesions, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

We propose a method for brain atlas deformation in presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. Finally, the seeded atlas is deformed, combining a method derived from optical flow principles and a model of lesion gr

Meritxell Bach Cuadra

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Region-based Satellite Image Classification: Method and Validation

Direct Fourier Tomographic Reconstruction Image-To-Image Filter

Segmentation of head and neck lymph node regions for radiotherapy planning, using active contour based atlas registration

A Surface-based approach to Quantify Local Cortical Gyrification

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

MIP: The ITS-EPFL Medical Image Processing Library

Ocular Structures Segmentation from Multi-sequences MRI Using 3D Unet with Fully Connected CRFs

Cross Validation of Experts Versus Registration Methods for Target Localization in Deep Brain Stimulation

Exporting Contours to DICOM-RT Structure Set

Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction

Towards a diffusion image processing validation and accuracy prediction framework

Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

Atlas-based Segmentation using a Model of Lesion Growth

Axonal and myelin injury in white matter and cortex of relapsing-remitting and progressive multiple sclerosis patients: a combined myelin water and multi-shell diffusion MRI study

Atlas-based Segmentation of Pathological MR Brain Images using a Model of Lesion Growth

Domain shift, domain adaptation, and generalization: A focus on MRI

Atlas-Based Segmentation of Pathological Brain MR Images

CLaiMS-Net: cortical lesions assessment in multiple sclerosis patients via a convolutional neural network and a single 3T MRI acquisition

Review on Atlas-based Segmentation of Magnetic Resonance Brain Images