Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients

Arterial calcification is an independent predictor of cardiovascular disease (CVD) events whereas thoracic aorta calcium (TAC) detection might anticipate extracoronary outcomes. In this work, we trained six convolutional neural networks (CNNs) to detect aortic calcifications and to automate the TAC score assessment in intermediate CVD risk patients. Cardiac computed tomography images from 1415 patients were analyzed together with their aortic geometry previously assessed. Orthogonal patches centered in each aortic candidate lesion were reconstructed and a dataset with 19,790 images (61% positives) was built. Three single-input 2D CNNs were trained using axial, coronal and sagittal patches together with two multi-input 2.5D CNNs combining the orthogonal patches and identifying their best regional combination (BRC) in terms of lesion location. Aortic calcifications were concentrated in the descending (66%) and aortic arch (26%) portions. The BRC of axial patches to detect ascending or aortic arch lesions and sagittal images for the descending portion had the best performance: 0.954 F1-Score, 98.4% sensitivity, 87% of the subjects correctly classified in their TAC category and an average false positive TAC score per patient of 30. A CNN that combined axial and sagittal patches depending on the candidate aortic location ensured an accurate TAC score prediction.Fil: Guilenea, Federico Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Casciaro, Mariano Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Pascaner, Ariel Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Soulat, Gilles. Hopital Europeen Georges Pompidou; FranciaFil: Mousseaux, Elie. Hopital Europeen Georges Pompidou; FranciaFil: Craiem, Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; Argentin

Integrating Deep Learning into Digital Rock Analysis Workflow

Digital Rock Analysis (DRA) has expanded our knowledge about natural phenomena in various geoscience specialties. DRA as an emerging technology has limitations including (1) the trade-off between the size of spatial domain and resolution, (2) methodological and human-induced errors in segmentation, and (3) the computational costs associated with intensive modeling. Deep learning (DL) methods are utilized to alleviate these limitations. First, two DL frameworks are utilized to probe the performance gains from using Convolutional Neural Networks (CNN) to super-resolve and segment real multi-resolution X-ray images of complex carbonate rocks. The first framework experiments the applications of U-Net and U-ResNet architectures to obtain macropore, solid, and micropore segmented images in an end-to-end scheme. The second framework segregates the super-resolution and segmentation into two networks: EDSR and U-ResNet. Both frameworks show consistent performance indicated by the voxel-wise accuracy metrics, the measured phase morphology, and flow characteristics. The end-to-end frameworks are shown to be superior to using a segregated approach confirming the adequacy of end-to-end learning for performing complex tasks. Second, CNNs accuracy margins in estimating physical properties of porous media 2d X-ray images are investigated. Binary and greyscale sandstone images are used as an input to CNNs architectures to estimate porosity, specific surface area, and average pore size of three sandstone images. The results show encouraging margins of accuracy where the error in estimating these properties can be up to 6% when using binary images and up to 7% when using greyscale images. Third, the suitability of CNNs as regression tools to predict a more challenging property, permeability, is investigated. Two complex CNNs architectures (ResNet and ResNext) are applied to learn the morphology of pore space in 3D porous media images for flow-based characterization. The dataset includes more than 29,000 3d subvolumes of multiple sandstone and carbonates rocks. The findings show promising regression accuracy using binary images. Accuracy gains are observed using conductivity maps as an input to the networks. Permeability inference on unseen samples can be achieved in 120 ms/sample with an average relative error of 18.9%. This thesis demonstrates the significant potential of deep learning in improving DRA capabilities

MACHINE LEARNING BASED ANALYSIS AND COMPUTER AIDED CLASSIFICATION OF NEUROPSYCHIATRIC DISORDERS USING NEUROIMAGING

Machine learning (ML) based analysis of neuroimages in neuropsychiatry context are advancing the understanding of neurobiological profiles and the pathological bases of neuropsychiatric disorders. Computational analysis and investigations on features derived from structural magnetic resonance imaging (sMRI) of the brain are used to quantify morphological or anatomical characteristics of the different regions of the brain that have role in several distinct brain functions. This helps in the realization of anatomical underpinnings of those disorders that cause brain atrophy. Structural neuroimaging data acquired from schizophrenia (SCZ), bipolar disorder (BD) patients and people who experienced psychosis for the first time, are used for the experiments presented in this thesis. The cerebral cortex (i.e., gray matter) of the brain is one of the most studied anatomical part using 'cortical-average-thickness' distribution feature in the literature. This helps in the realization of the anatomical underpinning of those mental illnesses that cause brain atrophy. To this regard, based on statistical background, 'cortical-skewness' feature, a novel digital imaging-derived neuroanatomical biomarker that could potentially assist in the differentiation of healthy control (HC) and patient groups is proposed and tested in this thesis. The core theme of machine intelligence relies in extracting and learning patterns of input data from experience. Classification is one of the task. In a basic set up, ML algorithms are trained using exemplary multivariate data features and its associated class labels, so that they could be able to create models and do predictive classification and other tasks. Considering the conundrum nature of psychiatric disorders, researchers in the field, could benefit from ML based analysis of complex brain patterns. Out of many, one task is computer aided classification (CAC). This is achieved by training the algorithms, these complex brain patterns and their corresponding diagnostic statistics manual (DSM) based clinical gold standard labels. Indeed, in the literature, supervised learning methods such as support vector machines (SVM) which follow inductive learning strategy are widely exploited and achieved interesting results. Observing this and due to the fact that the most widely available relevant anatomical features of the cortex such as thickness and volume values, could not be considered satisfactory features because of the heterogeneous nature of the human brain anatomy due to differences in age, gender etc., a contextual similarity based learning is proposed. This learning uses a transductive learning mechanism (i.e, learn a specific function for the problem at hand) instead of learning a general function to solve a specific problem. Based on this, it is adopted, a formulation of a semi supervised graph transduction (label propagation) algorithm based on the notions of game theory, where the consistent labeling is represented with Nash equilibrium, to tackle the problem of learning from neuroimages with subtle microscopic difference among different clinical groups. However, since such kind of algorithms heavily rely on the graph structure of the extracted features, we extended the classification procedure by introducing a pre-training phase based on a distance metric learning strategy with the aim of enhancing the contextual similarity of the images by providing a 'must belong in the same class' and 'must not belong in the same class' constraint from the available training data. This would result to increase intra-class similarity and decrease inter-class similarity. The proposed classification pipeline is used for searching anatomical biomarkers. With the goal of identifying potential neuroanatomical markers of a psychiatric disorder, it is aimed to develop a feature selection strategy taking into consideration the widely exploited cortical thickness and the proposed skewness feature, with the objective of searching a combination of features from all cortical regions of the brain that could maximize the possible differentiation among the different clinical groups Considering Research Domain Criteria (RDoC) framework developed by National Institute of Mental Health (NIMH) with the aim of developing biologically valid perspective of mental disorders by integrating multimodal sources, clinical interview scores and neuroimaging data are used with ML methods to tackle the challenging problem of differential classification of BD vs. SCZ. Finally, as deep learning methods are emerging with remarkable results in several application domains, we adopted this class of methods especially convolutional neural networks (CNNs) with a 3D approach, to extract volumetric neuroanatomical markers. CAC of first episode psychosis (FEP) is performed by exploiting the 3D complex spatial structure of the brain to identify key regions of the brain associated with the pathophysiology of FEP. Testing of individualized predictions with big dataset of 855 structural scans to identify possible markers of the disease is performed

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Visual Perception For Robotic Spatial Understanding

Humans understand the world through vision without much effort. We perceive the structure, objects, and people in the environment and pay little direct attention to most of it, until it becomes useful. Intelligent systems, especially mobile robots, have no such biologically engineered vision mechanism to take for granted. In contrast, we must devise algorithmic methods of taking raw sensor data and converting it to something useful very quickly. Vision is such a necessary part of building a robot or any intelligent system that is meant to interact with the world that it is somewhat surprising we don\u27t have off-the-shelf libraries for this capability. Why is this? The simple answer is that the problem is extremely difficult. There has been progress, but the current state of the art is impressive and depressing at the same time. We now have neural networks that can recognize many objects in 2D images, in some cases performing better than a human. Some algorithms can also provide bounding boxes or pixel-level masks to localize the object. We have visual odometry and mapping algorithms that can build reasonably detailed maps over long distances with the right hardware and conditions. On the other hand, we have robots with many sensors and no efficient way to compute their relative extrinsic poses for integrating the data in a single frame. The same networks that produce good object segmentations and labels in a controlled benchmark still miss obvious objects in the real world and have no mechanism for learning on the fly while the robot is exploring. Finally, while we can detect pose for very specific objects, we don\u27t yet have a mechanism that detects pose that generalizes well over categories or that can describe new objects efficiently. We contribute algorithms in four of the areas mentioned above. First, we describe a practical and effective system for calibrating many sensors on a robot with up to 3 different modalities. Second, we present our approach to visual odometry and mapping that exploits the unique capabilities of RGB-D sensors to efficiently build detailed representations of an environment. Third, we describe a 3-D over-segmentation technique that utilizes the models and ego-motion output in the previous step to generate temporally consistent segmentations with camera motion. Finally, we develop a synthesized dataset of chair objects with part labels and investigate the influence of parts on RGB-D based object pose recognition using a novel network architecture we call PartNet