Showing papers by "Miguel Ángel González Ballester published in 2017"

A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI

Abstract: Computer-aided diagnosis of cardiovascular diseases (CVDs) with cine-MRI is an important research topic to enable improved stratification of CVD patients. However, current approaches that use expert visualization or conventional clinical indices can lack accuracy for borderline classications. Advanced statistical approaches based on eigen-decomposition have been mostly concerned with shape and motion indices. In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large pools of radiomic features (statistical, shape and textural features) encoding relevant changes in anatomical and image characteristics due to CVDs. The calculated cine-MRI radiomic features are assessed using sequential forward feature selection to identify the most relevant ones for given CVD classes (e.g. myocardial infarction, cardiomyopathy, abnormal right ventricle). Finally, advanced machine learning is applied to suitably integrate the selected radiomics for final multi-feature classification based on Support Vector Machines (SVMs). The proposed technique was trained and cross-validated using 100 cine-MRI cases corresponding to five different cardiac classes from the ACDC MICCAI 2017 challenge (https://www.creatis.insa-lyon.fr/Challenge/acdc/index.html). All cases were correctly classified in this preliminary study, indicating potential of using large-scale radiomics for MRI-based diagnosis of CVDs.

Toward the automatic quantification of in utero brain development in 3D structural MRI: A review.

Abstract: Investigating the human brain in utero is important for researchers and clinicians seeking to understand early neurodevelopmental processes. With the advent of fast magnetic resonance imaging (MRI) techniques and the development of motion correction algorithms to obtain high-quality 3D images of the fetal brain, it is now possible to gain more insight into the ongoing maturational processes in the brain. In this article, we present a review of the major building blocks of the pipeline toward performing quantitative analysis of in vivo MRI of the developing brain and its potential applications in clinical settings. The review focuses on T1- and T2-weighted modalities, and covers state of the art methodologies involved in each step of the pipeline, in particular, 3D volume reconstruction, spatio-temporal modeling of the developing brain, segmentation, quantification techniques, and clinical applications. Hum Brain Mapp 38:2772-2787, 2017. © 2017 Wiley Periodicals, Inc.

A multiscale imaging and modelling dataset of the human inner ear

Abstract: Understanding the human inner ear anatomy and its internal structures is paramount to advance hearing implant technology. While the emergence of imaging devices allowed researchers to improve understanding of intracochlear structures, the difficulties to collect appropriate data has resulted in studies conducted with few samples. To assist the cochlear research community, a large collection of human temporal bone images is being made available. This data descriptor, therefore, describes a rich set of image volumes acquired using cone beam computed tomography and micro-CT modalities, accompanied by manual delineations of the cochlea and sub-compartments, a statistical shape model encoding its anatomical variability, and data for electrode insertion and electrical simulations. This data makes an important asset for future studies in need of high-resolution data and related statistical data objects of the cochlea used to leverage scientific hypotheses. It is of relevance to anatomists, audiologists, computer scientists in the different domains of image analysis, computer simulations, imaging formation, and for biomedical engineers designing new strategies for cochlear implantations, electrode design, and others.

Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures

Abstract: We describe a detector of robotic instrument parts in imageguided surgery. The detector consists of a huge ensemble of scale-variant and pose-dedicated, rigid appearance templates. The templates, which are equipped with pose-related keypoints and segmentation masks, allow for explicit pose estimation and segmentation of multiple end-effectors as well as fine-grained non-maximum suppression. We train the templates by grouping examples of end-effector articulations, imaged at various viewpoints, in thus arising space of instrument shapes. Proposed shapebased grouping forms tight clusters of pose-specific end-effector appearance. Experimental results show that the proposed method can effectively estimate the end-effector pose and delineate its boundary while being trained with moderately sized data clusters. We then show that matching such huge ensemble of templates takes less than one second on commodity hardware.

Early Prediction of Alzheimer’s Disease with Non-local Patch-Based Longitudinal Descriptors

Abstract: Alzheimer’s disease (AD) is characterized by a progressive decline in the cognitive functions accompanied by an atrophic process which can already be observed in the early stages using magnetic resonance images (MRI). Individualized prediction of future progression to AD, when patients are still in the mild cognitive impairment (MCI) stage, has potential impact for preventive treatment. Atrophy patterns extracted from longitudinal MRI sequences provide valuable information to identify MCI patients at higher risk of developing AD in the future. We present a novel descriptor that uses the similarity between local image patches to encode local displacements due to atrophy between a pair of longitudinal MRI scans. Using a conventional logistic regression classifier, our descriptor achieves \(76\%\) accuracy in predicting which MCI patients will progress to AD up to 3 years before conversion.

Automatic Labeling of Vascular Structures with Topological Constraints via HMM

Abstract: Identification of anatomical vessel branches is a prerequisite task for diagnosis, treatment and inter-subject comparison. We propose a novel graph labeling approach to anatomically label vascular structures of interest. Our method first extracts bifurcations of interest from the centerlines of vessels, where a set of geometric features are also calculated from. Then the probability distribution of every bifurcation is learned using a XGBoost classifier. Finally a Hidden Markov Model with a restricted transition strategy is constructed in order to find the most likely labeling configuration of the whole structure, while also enforcing topological consistency. In this paper, the proposed approach has been evaluated through leave-one-out cross validation on 50 subjects of centerlines obtained from MRA images of healthy volunteers’ Circle of Willis. Results demonstrate that our method can achieve higher accuracy and specificity, while obtaining similar precision and recall, when comparing to the best performing state-of-the-art methods. Our algorithm can handle different topologies, like circle, chain and tree. By using coordinate independent geometrical features, it does not require prior global alignment.

DCNN-Based Automatic Segmentation and Quantification of Aortic Thrombus Volume: Influence of the Training Approach

Abstract: Computerized Tomography Angiography (CTA) based assessment of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential during follow-up to evaluate the progress of the patient along time, comparing it to the pre-operative situation, and to detect complications. In this context, accurate assessment of the aneurysm or thrombus volume pre- and post-operatively is required. However, a quantifiable and trustworthy evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose an automatic pipeline for thrombus volume assessment, starting from its segmentation based on a Deep Convolutional Neural Network (DCNN) both pre-operatively and post-operatively. The aim is to investigate several training approaches to evaluate their influence in the thrombus volume characterization.

Efficient Column Generation for Cell Detection and Segmentation.

Abstract: We study the problem of instance segmentation in biological images with crowded and compact cells. We formulate this task as an integer program where variables correspond to cells and constraints enforce that cells do not overlap. To solve this integer program, we propose a column generation formulation where the pricing program is solved via exact optimization of very small scale integer programs. Column generation is tightened using odd set inequalities which fit elegantly into pricing problem optimization. Our column generation approach achieves fast stable anytime inference for our instance segmentation problems. We demonstrate on three distinct light microscopy datasets, with several hundred cells each, that our proposed algorithm rapidly achieves or exceeds state of the art accuracy.

Multi-scale immunological and biomechanical model of emphysema progression

Abstract: This work presents a multi-scale agent-based model of emphysema progression that includes both the slow action of the immune system and the fast action of force redistribution and fracture propagation of the biological tissue. The two scales are coupled because the immune response causes inflammation and adaptation, which affects the biomechanical parameters of the tissue such as his elasticity. During repeated inflammation and breathing cycles, the tissue weakens and breaks down. We found that macrophages lifespan and cytokynes diffusion ratio are the parameters that influence the outcome of the model the most.

Medial structure generation for registration of anatomical structures

Abstract: Medial structures (skeletons and medial manifolds) have shown capacity to describe shape in a compact way. In the field of medical imaging, they have been employed to enrich the description of organ anatomy, to improve segmentation, or to describe the organ position in relation to surrounding structures. Methods for generation of medial structures, however, are prone to the generation of medial artifacts (spurious branches) that traditionally need to be pruned before the medial structure can be used for further computations. The act of pruning can affect main sections of the medial surface, hindering its performance as shape descriptor. In this work, we present a method for the computation of medial structures that generates smooth medial surfaces that do not need to be explicitly pruned. Additionally, we present a validation framework for medial surface evaluation. Finally, we apply this method to create a parametric model of the cochlea shape that yields better registration results between cochleae.

On the Role of Patch Spaces in Patch-Based Label Fusion

Abstract: Multi-atlas segmentation has shown promising results in the segmentation of biomedical images. In the most common approach, registration is used to warp the atlases to the target space and then the warped atlas labelmaps are fused into a consensus segmentation. Label fusion in target space has shown to produce very accurate segmentations although at the expense of registering all atlases to each target image. Moreover, appearance and label information used by label fusion is extracted from the warped atlases, which are subject to interpolation errors. This work explores the role of extracting this information from the native spaces and adapt two label fusion approaches to this scheme. Results on the segmentation of subcortical brain structures indicate that using atlases in their native space yields superior performance than warping the atlases to the target. Moreover, using the native space lessens the computational requirements in terms of number of registrations and learning.

Estimating 3D ventricular shape from 2D echocardiography: feasibility and effect of noise

Abstract: Many cardiac diseases are associated with changes in ventricular shape. However, in daily practice, the heart is mostly assessed by 2D echocardiography only. While 3D techniques are available, they are rarely used. In this paper we analyze to which extent it is possible to obtain the 3D shape of a left ventricle (LV) using measurements from 2D echocardiography. First, we investigate this using synthetic datasets, and afterwards, we illustrate it in clinical 2D echocardiography measurements with corresponding 3D meshes obtained using 3D echocardiography. We demonstrate that standard measurements taken in 2D allow quantifying only the ellipsoidal shape of the ventricle, and that capturing other shape features require either additional geometrical measurements or clinical information related to shape remodelling. We show that noise in the measurements is the primary cause for poor association between the measurements and the LV shape features and that an estimated \(10\%\) level of noise on the 2D measurements limits the recoverability of shape. Finally we show that clinical variables relating to the clinical history can substitute the lack of geometric measurements, thus providing alternatives for shape assessment in daily practice.