183 research outputs found

    A Survey on Deep Learning in Medical Image Analysis

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    Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

    Over time RF fitting for Jitter Free 3D Vertebra Reconstruction from Video Fluoroscopy

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    Over the past decades, there has been an increasing interest in spine kinematics. Various approaches have been proposed on how to observe and analyse spine kinematics from a computer vision perspective. Amongst all, emphasis has been given to both the shape of the individual vertebrae as well as the overall spine curvature as a means of providing accurate and valid spinal condition diagnosis. Traditional invasive methods cannot accurately delineate the intersegmental motion of the spine vertebrae. On the contrary, capturing and measuring spinal motion via the non-invasive fluoroscopy has been a popular technique choice because of its low incurred patient radiation exposure nature. In general, image-based and other reconstruction methods target individual frames and focus on static spine instances. However, even the ones analysing sequences yield in unstable and jittery animations of the reconstructed spine. In this report, we address this issue using a novel approach to robustly reconstruct and rigidly derive a shape with no inter-frame variations. This is to produce animations that are jitter free across our sequence based on fluoroscopy video. Our main contributions are 1) retaining the shape of the solid vertebrae across the frame range, 2) helping towards a more accurate image segmentation even when there's a limited training set. We show our pipeline's success by reconstructing and comparing 3D animations of the lumbar spine from a corresponding fluoroscopic video

    Unsupervised domain adaptation for vertebrae detection and identification in 3D CT volumes using a domain sanity loss

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    A variety of medical computer vision applications analyze 2D slices of computed tomography (CT) scans, whereas axial slices from the body trunk region are usually identified based on their relative position to the spine. A limitation of such systems is that either the correct slices must be extracted manually or labels of the vertebrae are required for each CT scan to develop an automated extraction system. In this paper, we propose an unsupervised domain adaptation (UDA) approach for vertebrae detection and identification based on a novel Domain Sanity Loss (DSL) function. With UDA the model’s knowledge learned on a publicly available (source) data set can be transferred to the target domain without using target labels, where the target domain is defined by the specific setup (CT modality, study protocols, applied pre- and processing) at the point of use (e.g., a specific clinic with its specific CT study protocols). With our approach, a model is trained on the source and target data set in parallel. The model optimizes a supervised loss for labeled samples from the source domain and the DSL loss function based on domain-specific “sanity checks” for samples from the unlabeled target domain. Without using labels from the target domain, we are able to identify vertebra centroids with an accuracy of 72.8%. By adding only ten target labels during training the accuracy increases to 89.2%, which is on par with the current state-of-the-art for full supervised learning, while using about 20 times less labels. Thus, our model can be used to extract 2D slices from 3D CT scans on arbitrary data sets fully automatically without requiring an extensive labeling effort, contributing to the clinical adoption of medical imaging by hospitals

    CAD-Based Porous Scaffold Design of Intervertebral Discs in Tissue Engineering

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    With the development and maturity of three-dimensional (3D) printing technology over the past decade, 3D printing has been widely investigated and applied in the field of tissue engineering to repair damaged tissues or organs, such as muscles, skin, and bones, Although a number of automated fabrication methods have been developed to create superior bio-scaffolds with specific surface properties and porosity, the major challenges still focus on how to fabricate 3D natural biodegradable scaffolds that have tailor properties such as intricate architecture, porosity, and interconnectivity in order to provide the needed structural integrity, strength, transport, and ideal microenvironment for cell- and tissue-growth. In this dissertation, a robust pipeline of fabricating bio-functional porous scaffolds of intervertebral discs based on different innovative porous design methodologies is illustrated. Firstly, a triply periodic minimal surface (TPMS) based parameterization method, which has overcome the integrity problem of traditional TPMS method, is presented in Chapter 3. Then, an implicit surface modeling (ISM) approach using tetrahedral implicit surface (TIS) is demonstrated and compared with the TPMS method in Chapter 4. In Chapter 5, we present an advanced porous design method with higher flexibility using anisotropic radial basis function (ARBF) and volumetric meshes. Based on all these advanced porous design methods, the 3D model of a bio-functional porous intervertebral disc scaffold can be easily designed and its physical model can also be manufactured through 3D printing. However, due to the unique shape of each intervertebral disc and the intricate topological relationship between the intervertebral discs and the spine, the accurate localization and segmentation of dysfunctional discs are regarded as another obstacle to fabricating porous 3D disc models. To that end, we discuss in Chapter 6 a segmentation technique of intervertebral discs from CT-scanned medical images by using deep convolutional neural networks. Additionally, some examples of applying different porous designs on the segmented intervertebral disc models are demonstrated in Chapter 6

    Vertebral Compression Fracture Detection With Novel 3D Localisation

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    Vertebral compression fractures (VCF) often go undetected in radiology images, potentially leading to secondary fractures and permanent disability or even death. The objective of this thesis is to develop a fully automated method for detecting VCF in incidental CT images acquired for other purposes, thereby facilitating better follow up and treatment. The proposed approach is based on 3D localisation in CT images, followed by VCF detection in the localised regions. The 3D localisation algorithm combines deep reinforcement learning (DRL) with imitation learning (IL) to extract thoracic / lumbar spine regions from chest / abdomen CT scans. The algorithm generates six bounding boxes as Regions of Interest (ROI) using three different CNN models, with an average Jaccard Index (JI)/Dice Coefficient (DC) of 74.21%/84.71%. The extracted ROI were then divided into slices and the slices into patches to train four convolutional neural network (CNN) models for VCF detection at the patch level. The predictions from the patches were aggregated at bounding box level, and majority voting performed to decide on the presence / absence of VCF for a patient. The best performing model was a six layered CNN, which together with majority voting achieved threefold cross validation accuracy / F1 Score of 85.95% / 85.94% from 308 chest scans. The same model also achieved a fivefold cross validation accuracy / F1 score of 86.67% / 87.04% from 168 abdomen scans. Because of the success of the 3D localisation algorithm, it was also trained on other abdominal organs, namely the spleen and left and right kidneys, with promising results. The 3D localisation algorithm was enhanced to work with fused bounding boxes and also in semi-supervised mode to address the problem of annotation time by radiologists. Experiments using three different proportions of labelled and unlabelled data achieved fairly good performance, although not as good as the fully supervised equivalents. Finally, VCF detection in a weakly supervised multiple instance learning (MIL) setting was performed to reduce radiologists’ time for annotations, together with majority voting on the six bounding boxes. The best performing model was the six layered CNN which achieved threefold cross validation accuracy / F1 score of 81.05% / 80.74 % on 308 thoracic scans, and fivefold cross validation accuracy / F1 Score of 85.45% / 86.61% on 168 abdomen scans. Overall, the results are comparable to the state-of the art that used an order of magnitude more scans
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