Showing papers by "Miguel Ángel González Ballester published in 2019"
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
TL;DR: This paper proposes a neural network framework that incorporates multi-scale and multi-level learning, capable of learning complex hierarchical features to resolve spatial ambiguity in estimating the pectoral muscle boundary, modified by the Holistically-nested Edge Detection network.
58 citations
TL;DR: A review of the state of the art on multi-organ analysis and associated computation anatomy methodology is presented in this article, with a methodology-based classification of different techniques available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches.
44 citations
TL;DR: This work proposes a novel fully‐automated method to segment the placenta and its peripheral blood vessels from fetal MRI, and suggests that this methodology can aid the diagnosis and surgical planning of severe fetal disorders.
33 citations
13 Oct 2019
TL;DR: In this paper, the authors proposed and compared several strategies relying on curriculum learning, to support the classification of proximal femur fracture from X-ray images, a challenging problem as reflected by existing intra-and inter-expert disagreement.
Abstract: Current deep-learning based methods do not easily integrate to clinical protocols, neither take full advantage of medical knowledge. In this work, we propose and compare several strategies relying on curriculum learning, to support the classification of proximal femur fracture from X-ray images, a challenging problem as reflected by existing intra- and inter-expert disagreement. Our strategies are derived from knowledge such as medical decision trees and inconsistencies in the annotations of multiple experts, which allows us to assign a degree of difficulty to each training sample. We demonstrate that if we start learning “easy” examples and move towards “hard”, the model can reach a better performance, even with fewer data. The evaluation is performed on the classification of a clinical dataset of about 1000 X-ray images. Our results show that, compared to class-uniform and random strategies, the proposed medical knowledge-based curriculum, performs up to 15% better in terms of accuracy, achieving the performance of experienced trauma surgeons.
26 citations
TL;DR: This work proposes a multimodal image distance measure based on the commutativity of graph Laplacians and shows on synthetic and real datasets that this approach is applicable to dense rigid and non-rigid image registration.
26 citations
TL;DR: This work proposes a novel data‐driven method for parcellation of fetal cortical surface atlases into distinct regions based on the dynamic “growth patterns” of cortical properties from a population of fetuses, and reveals spatially contiguous, hierarchical and bilaterally relatively symmetric patterns of fetal cortex surface development.
Abstract: Defining anatomically and functionally meaningful parcellation maps on cortical surface atlases is of great importance in surface-based neuroimaging analysis. The conventional cortical parcellation maps are typically defined based on anatomical cortical folding landmarks in adult surface atlases. However, they are not suitable for fetal brain studies, due to dramatic differences in brain size, shape, and properties between adults and fetuses. To address this issue, we propose a novel data-driven method for parcellation of fetal cortical surface atlases into distinct regions based on the dynamic "growth patterns" of cortical properties (e.g., surface area) from a population of fetuses. Our motivation is that the growth patterns of cortical properties indicate the underlying rapid changes of microstructures, which determine the molecular and functional principles of the cortex. Thus, growth patterns are well suitable for defining distinct cortical regions in development, structure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices, and the other is based on the correlation profiles of vertices' growth trajectories in relation to a set of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better capture both their common and complementary information than by simply averaging them. Finally, based on this fused similarity matrix, we perform spectral clustering to divide the fetal cortical surface atlases into distinct regions. By applying our method on 25 normal fetuses from 26 to 29 gestational weeks, we construct age-specific fetal cortical surface atlases equipped with biologically meaningful parcellation maps based on cortical growth patterns. Importantly, our generated parcellation maps reveal spatially contiguous, hierarchical and bilaterally relatively symmetric patterns of fetal cortical surface development.
24 citations
13 Oct 2019
TL;DR: In this paper, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities.
Abstract: Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences.
19 citations
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TL;DR: The results based on three magnetic resonance sequences show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets.
Abstract: Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences.
19 citations
TL;DR: 3D FE models derived from DXA scans might significantly improve the prediction of hip fracture risk; providing a new insight for clinicians to use FE simulations in clinical practice for osteoporosis management.
18 citations
TL;DR: The proposed TTTS fetal surgery planning and simulation platform is integrated into a flexible C++ and MITK-based application to provide a full exploration of the intrauterine environment by simulating the fetoscope camera as well as the laser ablation.
TL;DR: 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 toCVDs is presented.
Abstract: 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 \footnote{this https URL}. All cases were correctly classified in this preliminary study, indicating potential of using large-scale radiomics for MRI-based diagnosis of CVDs.
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TL;DR: This work aims at proposing the first 3D convolutional neural network for the segmentation of aneurysms both from preoperative and postoperative CTA scans, suitable to automatically determine the AAA diameter and opens up the opportunity for more complex aneurYSm analysis.
Abstract: An abdominal aortic aneurysm (AAA) is a focal dilation of the aorta that, if not treated, tends to grow and may rupture. A significant unmet need in the assessment of AAA disease, for the diagnosis, prognosis and follow-up, is the determination of rupture risk, which is currently based on the manual measurement of the aneurysm diameter in a selected Computed Tomography Angiography (CTA) scan. However, there is a lack of standardization determining the degree and rate of disease progression, due to the lack of robust, automated aneurysm segmentation tools that allow quantitatively analyzing the AAA. In this work, we aim at proposing the first 3D convolutional neural network for the segmentation of aneurysms both from preoperative and postoperative CTA scans. We extensively validate its performance in terms of diameter measurements, to test its applicability in the clinical practice, as well as regarding the relative volume difference, and Dice and Jaccard scores. The proposed method yields a mean diameter measurement error of 3.3 mm, a relative volume difference of 8.58 %, and Dice and Jaccard scores of 87 % and 77 %, respectively. At a clinical level, an aneurysm enlargement of 10 mm is considered relevant, thus, our method is suitable to automatically determine the AAA diameter and opens up the opportunity for more complex aneurysm analysis.
TL;DR: This work proposes a promising methodology for post-operative CTA time-series registration and subsequent aneurysm biomechanical strain analysis, which yields a mean area under the curve of 88.6% when correlating the strain-based quantitative descriptors with the long-term patient prognosis.
Abstract: An abdominal aortic aneurysm (AAA) is a focal dilation of the abdominal aorta, that if not treated, tends to grow and may rupture. The most common treatment for AAAs is the endovascular aneurysm repair (EVAR), which requires that patients undergo Computed Tomography Angiography (CTA)-based post-operative lifelong surveillance due to the possible appearance of complications. These complications may again lead to AAA dilation and rupture. However, there is a lack of advanced quantitative image-analysis tools to support the clinicians in the follow-up. Currently, the approach is to evaluate AAA diameter changes along time to infer the progress of the patient and the post-operative risk of AAA rupture. An increased AAA diameter is usually associated with a higher rupture risk, but there are some small AAAs that rupture, whereas other larger aneurysms remain stable. This means that the diameter-based rupture risk assessment is not suitable for all the cases, and there is increasing evidence that the biomechanical behavior of the AAA may provide additional valuable information regarding the progression of the disease and the risk of rupture. Hence, we propose a promising methodology for post-operative CTA time-series registration and subsequent aneurysm biomechanical strain analysis. From these strains, quantitative image-based descriptors are extracted using a principal component analysis of the tensile and compressive strain fields. Evaluated on 22 patients, our approach yields a mean area under the curve of 88.6% when correlating the strain-based quantitative descriptors with the long-term patient prognosis. This suggests that the strain information directly extracted from the CTA images is able to capture the biomechanical behavior of the aneurysm without relying on finite element modeling and simulation. Furthermore, the extracted descriptors set the basis for possible future imaging biomarkers that may be used in clinical practice. Apart from the diameter, these biomarkers may be used to assess patient prognosis and to enable informed decision making after an EVAR intervention, especially in difficult uncertain cases.
TL;DR: The results show that daily situations of stress can lead to decreased stability, and the need to explicitly explore the troubling fact that a large portion of population might not be able to properly breath is highlighted.
24 Nov 2019
TL;DR: This paper presents a meta-analyses of the immune checkpoints of the autonomic nervous system and its role in the development of central nervous system disease and its consequences.
Abstract: 1 August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain 2 University Pompeu Fabra, Department of Information and Communication Technologies, Barcelona, Spain 3 University of Zagreb School of Medicine, Department of Cardiovascular Diseases, Zagreb, Croatia 4 Joint Research Centre, European Commission, Brussels, Belgium 5 Computer Science Department, Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center, Universitat Politecnica de Catalunya, Barcelona, Spain 6 Wales Heart Research Institute, School of Medicine, Cardiff University, Cardiff, UK 7 ICREA, Barcelona, Spain
TL;DR: In this article, the authors presented a design of a compact antenna applicator for a microwave colonoscopy system, which consists of one transmitting and one receiving cavity-backed U-shaped slot antenna elements fed by an L-shaped microstrip line.
Abstract: This paper presents a design of a compact antenna applicator for a microwave colonoscopy system. Although colonoscopy is the most effective method for colorectal cancer detection, it suffers from important visualization restrictions that limit its performance. We recently reported that the contrast between healthy mucosa and cancer was 30%–100% for the relative permittivity and conductivity, respectively, at 8 GHz, and the complex permittivity increased proportionally to the degeneration rate of polyps (cancer precursors). The applicator is designed as a compact cylindrical array of eight antennas attached at the tip of a conventional colonoscope. The design presented here is a proof-of-concept applicator composed by one transmitting and one receiving cavity-backed U-shaped slot antenna elements fed by an L-shaped microstrip line. The antennas are low profile and present a high isolation at 8 GHz. The antenna performance is assessed with simulations and experimentally with a phantom composed by different liquids.
08 Apr 2019
TL;DR: In this article, a radiomics approach for identifying intermediate imaging phenotypes associated with hypertension is described, which combines feature selection and machine learning techniques to identify the most subtle as well as complex structural and tissue changes in hypertensive subgroups as compared to healthy individuals.
Abstract: Hypertension is a medical condition that is well-established as a risk factor for many major diseases. For example, it can cause alterations in the cardiac structure and function over time that can lead to heart related morbidity and mortality. However, at the subclinical stage, these changes are subtle and cannot be easily captured using conventional cardiovascular indices calculated from clinical cardiac imaging. In this paper, we describe a radiomics approach for identifying intermediate imaging phenotypes associated with hypertension. The method combines feature selection and machine learning techniques to identify the most subtle as well as complex structural and tissue changes in hypertensive subgroups as compared to healthy individuals. Validation based on a sample of asymptomatic hearts that include both hypertensive and non-hypertensive cases demonstrate that the proposed radiomics model is capable of detecting intensity and textural changes well beyond the capabilities of conventional imaging phenotypes, indicating its potential for improved understanding of the longitudinal effects of hypertension on cardiovascular health and disease.
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TL;DR: In this article, a 3D siamese neural network was used to detect, match, and predict nodule growth given pairs of CT scans of the same patient without the need for image registration.
Abstract: Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists. Several lung CT scan images taken at different time points of a given patient need to be individually inspected, looking for possible cancerogenous nodules. Radiologists mainly focus their attention in nodule size, density, and growth to assess the existence of malignancy. In this study, we present a novel method based on a 3D siamese neural network, for the re-identification of nodules in a pair of CT scans of the same patient without the need for image registration. The network was integrated into a two-stage automatic pipeline to detect, match, and predict nodule growth given pairs of CT scans. Results on an independent test set reported a nodule detection sensitivity of 94.7%, an accuracy for temporal nodule matching of 88.8%, and a sensitivity of 92.0% with a precision of 88.4% for nodule growth detection.
08 Apr 2019
TL;DR: A fully-automated framework to achieve an accurate segmentation of the placenta and its peripheral vasculature in 3D US for twin-to-twin transfusion syndrome is proposed for the first time.
Abstract: Twin-to-twin transfusion syndrome is a serious condition that can affect pregnancies when identical twins share the placenta. In these cases, abnormal placental vessel connections (anastomoses) cause an uneven blood distribution between the babies. Ultrasound (US) enormously facilitates the assessment of these cases, but placenta segmentation is still a challenging task due to artifacts and high variability in its position, orientation, shape and appearance. We propose for the first time a fully-automated framework to achieve an accurate segmentation of the placenta and its peripheral vasculature in 3D US. A conditional Generative Adversarial Network is used to automatically identify the placenta. Afterwards, the entire vasculature is extracted using Modified Spatial Kernelized Fuzzy C-Means and Markov Random Fields. The method is tested on singleton and twin pregnancies from 15 to 38 gestational weeks, achieving a mean Dice coefficient of 0.75 ± 0.12 and 0.70 ± 0.14 for the placenta and its vessels.
TL;DR: The patch-based label fusion framework is revisited and extended, exploring the role of extracting appearance and label information from the native space of both atlases and target images, thus avoiding interpolation artifacts, and results indicate that using atlas patches in their native space yields superior performance than warping the atlased to the target image.
13 Oct 2019
TL;DR: Results show that the performance of a CNN trained with synthetic data to segment AAAs from new scans is comparable to the one of a network trained with real images, which suggests that the proposed methodology may be applied to generate images and train a CNN to segment other types of aneurysms, reducing the burden of obtaining large annotated image databases.
Abstract: An abdominal aortic aneurysm (AAA) is a ballooning of the abdominal aorta, that if not treated tends to grow and rupture. Computed Tomography Angiography (CTA) is the main imaging modality for the management of AAAs, and segmenting them is essential for AAA rupture risk and disease progression assessment. Previous works have shown that Convolutional Neural Networks (CNNs) can accurately segment AAAs, but have the limitation of requiring large amounts of annotated data to train the networks. Thus, in this work we propose a methodology to train a CNN only with images generated with a synthetic shape model, and test its generalization and ability to segment AAAs from new original CTA scans. The synthetic images are created from realistic deformations generated by applying principal component analysis to the deformation fields obtained from the registration of few datasets. The results show that the performance of a CNN trained with synthetic data to segment AAAs from new scans is comparable to the one of a network trained with real images. This suggests that the proposed methodology may be applied to generate images and train a CNN to segment other types of aneurysms, reducing the burden of obtaining large annotated image databases.
TL;DR: Two methods for detecting and tracking cells using the open-source Fiji and ilastik frameworks are presented, consisting of a pre-processing and segmentation phase followed by a tracking phase, based on the overlapping of objects along the image sequence.
Abstract: Tracking cells is one of the main challenges in biology, as it often requires time-consuming annotations and the images can have a low signal-to-noise ratio while containing a large number of cells. Here we present two methods for detecting and tracking cells using the open-source Fiji and ilastik frameworks. A straightforward approach is described using Fiji, consisting of a pre-processing and segmentation phase followed by a tracking phase, based on the overlapping of objects along the image sequence. Using ilastik, a classifier is trained through manual annotations to both detect cells over the background and be able to recognize false detections and merging cells. We describe these two methods in a step-by-step fashion, using as example a time-lapse microscopy movie of HeLa cells.