61 research outputs found

    Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data

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    We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an image analysis pipeline. Since deep neural networks have made a large impact on the field of image processing in the past years, we use two different deep learning architectures to segment the urinary bladder. Both of these architectures are based on pre-trained classification networks that are adapted to perform semantic segmentation. Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data. This is done by applying thresholding to the Positron Emission Tomography data for obtaining a ground truth and by utilizing data augmentation to enlarge the dataset. In this study, we discuss the influence of data augmentation on the segmentation results, and compare and evaluate the proposed architectures in terms of qualitative and quantitative segmentation performance. The results presented in this study allow concluding that deep neural networks can be considered a promising approach to segment the urinary bladder in CT images.Comment: 20 page

    Label Efficient Deep Learning in Medical Imaging

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    Recent state-of-the-art deep learning frameworks require large, fully annotated training datasets that are, depending on the objective, time-consuming to generate. While in most fields, these labelling tasks can be parallelized massively or even outsourced, this is not the case for medical images. Usually, only a highly trained expert is able to generate these datasets. However, since additional manual annotation, especially for the purpose of segmentation or tracking, is typically not part of a radiologist's workflow, large and fully annotated datasets are a rare and scarce good. In this context, a variety of frameworks are proposed in this work to solve the problems that arise due to the lack of annotated training data across different medical imaging tasks and modalities. The first contribution as part of this thesis was to investigate weakly supervised learning on PET/CT data for the task of lesion segmentation. Using only class labels (tumor vs. no tumor), a classifier was first trained and subsequently used to generate Class Activation Maps highlighting regions with lesions. Based on these region proposals, final tumor segmentation could be performed with high accuracy in clinically relevant metrics. This drastically simplifies the process of training data generation, as only class labels have to be assigned to each slice of a scan instead of a full pixel-wise segmentation. To further reduce the time required to prepare training data, two self-supervised methods were investigated for the task of anatomical tissue segmentation and landmark detection. To this end, as a second contribution, a state-of-the-art tracking framework based on contrastive random walks was transferred, adapted and extended to the medical imaging domain. As contrastive learning often lacks real-time capability, a self-supervised template matching network was developed to address the task of real-time anatomical tissue tracking, yielding the third contribution of this work. Both of these methods have in common that only during inference the object or region of interest is defined, reducing the number of required labels to as few as one and allowing adaptation to different tasks without having to re-train or access the original training data. Despite the limited amount of labelled data, good results could be achieved for both tracking of organs across subjects as well as tissue tracking within time-series. State-of-the-art self-supervised learning in medical imaging is usually performed on 2D slices due to the lack of training data and limited computational resources. To exploit the three-dimensional structure of this type of data, self-supervised contrastive learning was performed on entire volumes using over 40,000 whole-body MRI scans forming the fourth contribution. Due to this pre-training, a large number of downstream tasks could be successfully addressed using only limited labelled data. Furthermore, the learned representations allows to visualize the entire dataset in a two-dimensional view. To encourage research in the field of automated lesion segmentation in PET/CT image data, the autoPET challenge was organized, which represents the fifth contribution

    Image Processing and Analysis for Preclinical and Clinical Applications

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    Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis

    12 Chapters on Nuclear Medicine

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    The development of nuclear medicine as a medical specialty has resulted in the large-scale application of its effective imaging methods in everyday practice as a primary method of diagnosis. The introduction of positron-emitting tracers (PET) has represented another fundamental leap forward in the ability of nuclear medicine to exert a profound impact on patient management, while the ability to produce radioisotopes of different elements initiated a variety of tracer studies in biology and medicine, facilitating enhanced interactions of nuclear medicine specialists and specialists in other disciplines. At present, nuclear medicine is an essential part of diagnosis of many diseases, particularly in cardiologic, nephrologic and oncologic applications and it is well-established in its therapeutic approaches, notably in the treatment of thyroid cancers. Data from official sources of different countries confirm that more than 10-15 percent of expenditures on clinical imaging studies are spent on nuclear medicine procedures

    Developing multiparametric and novel magnetic resonance imaging biomarkers for prostate cancer

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    Whilst biomarker research is gaining momentum within the cancer sciences, disappointingly few biomarkers are successfully translated into clinical practice, which is partly due to lack of rigorous methodology. In this thesis, I aim to systematically study several quantitative magnetic resonance imaging (MRI) biomarkers (QIBs), at various stages of biomarker development for use as tools in the assessment of local and metastatic prostate cancer according to clinical need. I initially focus on QIBs derived from conventional multiparametric (mp) prostate MRI sequences, namely T2 weighted (T2W), apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE). Firstly, by optimising analytical methods used throughout the thesis, deciding which approach is more reliable between single-slice region-of-interest vs. contouring the whole tumour volume using two different software packages. I then consider whether metric reproducibility can be improved by normalisation to different anatomical structures, and assess whether it is preferable to use statistics derived from imaging histograms rather than the current convention of using mean values. I combine multiple QIBs in a logistic regression model to predict a Gleason 4 component in known prostate cancer, which represents an unmet clinical need, as noninvasive tools to distinguish these more aggressive tumours do not currently exist. I subsequently ‘technically validate’ a novel microstructural diffusion-weighted MRI technique called VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours) to detect aggressive prostate cancer as part of a prospective cohort study. I assess the image quality, contrast-to-noise ratio, repeatability and performance of quantitative parametric VERDICT maps to discriminate between Gleason grades vs. the current best performing, but still imperfect tool of ADC. In the final two results chapters, motivated by the limited diagnostic accuracy of the prostate cancer staging modalities in current clinical use, I investigate the ability of mp whole-body (WB) MRI to stage aggressive cancer outside the prostate in patients with a high risk of metastases at primary diagnosis, and in biochemical failure following prostatectomy

    [<sup>18</sup>F]fluorination of biorelevant arylboronic acid pinacol ester scaffolds synthesized by convergence techniques

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    Aim: The development of small molecules through convergent multicomponent reactions (MCR) has been boosted during the last decade due to the ability to synthesize, virtually without any side-products, numerous small drug-like molecules with several degrees of structural diversity.(1) The association of positron emission tomography (PET) labeling techniques in line with the “one-pot” development of biologically active compounds has the potential to become relevant not only for the evaluation and characterization of those MCR products through molecular imaging, but also to increase the library of radiotracers available. Therefore, since the [18F]fluorination of arylboronic acid pinacol ester derivatives tolerates electron-poor and electro-rich arenes and various functional groups,(2) the main goal of this research work was to achieve the 18F-radiolabeling of several different molecules synthesized through MCR. Materials and Methods: [18F]Fluorination of boronic acid pinacol esters was first extensively optimized using a benzaldehyde derivative in relation to the ideal amount of Cu(II) catalyst and precursor to be used, as well as the reaction solvent. Radiochemical conversion (RCC) yields were assessed by TLC-SG. The optimized radiolabeling conditions were subsequently applied to several structurally different MCR scaffolds comprising biologically relevant pharmacophores (e.g. β-lactam, morpholine, tetrazole, oxazole) that were synthesized to specifically contain a boronic acid pinacol ester group. Results: Radiolabeling with fluorine-18 was achieved with volumes (800 μl) and activities (≤ 2 GBq) compatible with most radiochemistry techniques and modules. In summary, an increase in the quantities of precursor or Cu(II) catalyst lead to higher conversion yields. An optimal amount of precursor (0.06 mmol) and Cu(OTf)2(py)4 (0.04 mmol) was defined for further reactions, with DMA being a preferential solvent over DMF. RCC yields from 15% to 76%, depending on the scaffold, were reproducibly achieved. Interestingly, it was noticed that the structure of the scaffolds, beyond the arylboronic acid, exerts some influence in the final RCC, with electron-withdrawing groups in the para position apparently enhancing the radiolabeling yield. Conclusion: The developed method with high RCC and reproducibility has the potential to be applied in line with MCR and also has a possibility to be incorporated in a later stage of this convergent “one-pot” synthesis strategy. Further studies are currently ongoing to apply this radiolabeling concept to fluorine-containing approved drugs whose boronic acid pinacol ester precursors can be synthesized through MCR (e.g. atorvastatin)

    Infective/inflammatory disorders

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    The radiological investigation of musculoskeletal tumours : chairperson's introduction

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