269 research outputs found

    Learning Algorithms for Fat Quantification and Tumor Characterization

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    Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

    Computational methods for the analysis of functional 4D-CT chest images.

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    Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

    Radiomic data mining and machine learning on preoperative pituitary adenoma MRI

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    Pituitary adenomas are among the most frequent intracranial tumors, accounting for the majority of sellar/suprasellar masses in adults. MRI is the preferred imaging modality for detecting pituitary adenomas. Radiomics represents the conversion of digital medical images into mineable high-dimensional data. This process is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology and that these relationships can be revealed via quantitative image analyses. The aim of this thesis is to apply machine learning algorithms on parameters obtained by texture analysis on MRI images in order to distinguish functional from non-functional pituitary macroadenomas, to predict their ki-67 proliferation index class, and to predict pituitary macroadenoma surgical consistency prior to an endoscopic endonasal procedure

    Optimization of computer-assisted intraoperative guidance for complex oncological procedures

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    MenciĂłn Internacional en el tĂ­tulo de doctorThe role of technology inside the operating room is constantly increasing, allowing surgical procedures previously considered impossible or too risky due to their complexity or limited access. These reliable tools have improved surgical efficiency and safety. Cancer treatment is one of the surgical specialties that has benefited most from these techniques due to its high incidence and the accuracy required for tumor resections with conservative approaches and clear margins. However, in many cases, introducing these technologies into surgical scenarios is expensive and entails complex setups that are obtrusive, invasive, and increase the operative time. In this thesis, we proposed convenient, accessible, reliable, and non-invasive solutions for two highly complex regions for tumor resection surgeries: pelvis and head and neck. We explored how the introduction of 3D printing, surgical navigation, and augmented reality in these scenarios provided high intraoperative precision. First, we presented a less invasive setup for osteotomy guidance in pelvic tumor resections based on small patient-specific instruments (PSIs) fabricated with a desktop 3D printer at a low cost. We evaluated their accuracy in a cadaveric study, following a realistic workflow, and obtained similar results to previous studies with more invasive setups. We also identified the ilium as the region more prone to errors. Then, we proposed surgical navigation using these small PSIs for image-to-patient registration. Artificial landmarks included in the PSIs substitute the anatomical landmarks and the bone surface commonly used for this step, which require additional bone exposure and is, therefore, more invasive. We also presented an alternative and more convenient installation of the dynamic reference frame used to track the patient movements in surgical navigation. The reference frame is inserted in a socket included in the PSIs and can be attached and detached without losing precision and simplifying the installation. We validated the setup in a cadaveric study, evaluating the accuracy and finding the optimal PSI configuration in the three most common scenarios for pelvic tumor resection. The results demonstrated high accuracy, where the main source of error was again incorrect placements of PSIs in regular and homogeneous regions such as the ilium. The main limitation of PSIs is the guidance error resulting from incorrect placements. To overcome this issue, we proposed augmented reality as a tool to guide PSI installation in the patient’s bone. We developed an application for smartphones and HoloLens 2 that displays the correct position intraoperatively. We measured the placement errors in a conventional and a realistic phantom, including a silicone layer to simulate tissue. The results demonstrated a significant reduction of errors with augmented reality compared to freehand placement, ensuring an installation of the PSI close to the target area. Finally, we proposed three setups for surgical navigation in palate tumor resections, using optical trackers and augmented reality. The tracking tools for the patient and surgical instruments were fabricated with low-cost desktop 3D printers and designed to provide less invasive setups compared to previous solutions. All setups presented similar results with high accuracy when tested in a 3D-printed patient-specific phantom. They were then validated in the real surgical case, and one of the solutions was applied for intraoperative guidance. Postoperative results demonstrated high navigation accuracy, obtaining optimal surgical outcomes. The proposed solution enabled a conservative surgical approach with a less invasive navigation setup. To conclude, in this thesis we have proposed new setups for intraoperative navigation in two complex surgical scenarios for tumor resection. We analyzed their navigation precision, defining the optimal configurations to ensure accuracy. With this, we have demonstrated that computer-assisted surgery techniques can be integrated into the surgical workflow with accessible and non-invasive setups. These results are a step further towards optimizing the procedures and continue improving surgical outcomes in complex surgical scenarios.Programa de Doctorado en Ciencia y TecnologĂ­a BiomĂ©dica por la Universidad Carlos III de MadridPresidente: RaĂșl San JosĂ© EstĂ©par.- Secretario: Alba GonzĂĄlez Álvarez.- Vocal: Simon Droui
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