477 research outputs found

    Unsupervised 3D Brain Anomaly Detection

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    Anomaly detection (AD) is the identification of data samples that do not fit a learned data distribution. As such, AD systems can help physicians to determine the presence, severity, and extension of a pathology. Deep generative models, such as Generative Adversarial Networks (GANs), can be exploited to capture anatomical variability. Consequently, any outlier (i.e., sample falling outside of the learned distribution) can be detected as an abnormality in an unsupervised fashion. By using this method, we can not only detect expected or known lesions, but we can even unveil previously unrecognized biomarkers. To the best of our knowledge, this study exemplifies the first AD approach that can efficiently handle volumetric data and detect 3D brain anomalies in one single model. Our proposal is a volumetric and high-detail extension of the 2D f-AnoGAN model obtained by combining a state-of-the-art 3D GAN with refinement training steps. In experiments using non-contrast computed tomography images from traumatic brain injury (TBI) patients, the model detects and localizes TBI abnormalities with an area under the ROC curve of ~75%. Moreover, we test the potential of the method for detecting other anomalies such as low quality images, preprocessing inaccuracies, artifacts, and even the presence of post-operative signs (such as a craniectomy or a brain shunt). The method has potential for rapidly labeling abnormalities in massive imaging datasets, as well as identifying new biomarkers.Comment: Accepted at BrainLes Workshop in MICCAI 202

    Human treelike tubular structure segmentation: A comprehensive review and future perspectives

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    Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed

    Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives

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    Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa

    Comparative lipidomic analysis of mammalian retinal ganglion cells and Muller glia in situ and in vitro using High-Resolution Imaging Mass Spectrometry

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    In order to better understand retinal physiology, alterations to which underlie some ocular diseases, we set out to establish the lipid signature of two fundamental cell types in the retina, Muller Glia and Retinal Ganglion Cells (RGCs). Moreover, we compared the lipid signature of these cells in sections (in situ), as well as after culturing the cells and isolating their cell membranes (in vitro). The lipidome of Muller glia and RGCs was analyzed in porcine retinal sections using Matrix Assisted Laser Desorption Ionization Imaging Mass Spectrometry (MALDI-IMS). Isolated membranes, as well as whole cells from primary cell cultures of RGCs and Muller glia, were printed onto glass slides using a non-contact microarrayer (Nano Plotter), and a LTQ-Orbitrap XL analyzer was used to scan the samples in negative ion mode, thereafter identifying the RGCs and Muller cells immunohistochemically. The spectra acquired were aligned and normalized against the total ion current, and a statistical analysis was carried out to select the lipids specific to each cell type in the retinal sections and microarrays. The peaks of interest were identified by MS/MS analysis. A cluster analysis of the MS spectra obtained from the retinal sections identified regions containing RGCs and Muller glia, as confirmed by immunohistochemistry in the same sections. The relative density of certain lipids differed significantly (p-value <= 0.05) between the areas containing Muller glia and RGCs. Likewise, different densities of lipids were evident between the RGC and Muller glia cultures in vitro. Finally, a comparative analysis of the lipid profiles in the retinal sections and microarrays identified six peaks that corresponded to a collection of 10 lipids characteristic of retinal cells. These lipids were identified by MS/MS. The analyses performed on the RGC layer of the retina, on RGCs in culture and using cell membrane microarrays of RGCs indicate that the lipid composition of the retina detected in sections is preserved in primary cell cultures. Specific lipid species were found in RGCs and Muller glia, allowing both cell types to be identified by a lipid fingerprint. Further studies into these specific lipids and of their behavior in pathological conditions may well help identify novel therapeutic targets for ocular diseases.This study was supported by the grants RETOS MINECO FEDER (RTC-2016-48231), PUE 2018-1-0004, UPV/EHU PPGA 18/18 and Elkartek KK-2019/00086 to E.V

    Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images.

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    Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model

    Exploring variability in medical imaging

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    Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces
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