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PURPOSE: In interstitial high-dose rate brachytherapy, liver cancer is treated by internal radiation, requiring percutaneous placement of applicators within or close to the tumor. To maximize utility, the optimal applicator configuration is pre-planned on magnetic resonance images. The pre-planned configuration is then implemented via a magnetic resonance-guided intervention. Mapping the pre-planning information onto interventional data would reduce the radiologist's cognitive load during the intervention and could possibly minimize discrepancies between optimally pre-planned and actually placed applicators. METHODS: We propose a fast and robust two-step registration framework suitable for interventional settings: first, we utilize a multi-resolution rigid registration to correct for differences in patient positioning (rotation and translation). Second, we employ a novel iterative approach alternating between bias field correction and Markov random field deformable registration in a multi-resolution framework to compensate for non-rigid movements of the liver, the tumors and the organs at risk. In contrast to existing pre-correction methods, our multi-resolution scheme can recover bias field artifacts of different extents at marginal computational costs. RESULTS: We compared our approach to deformable registration via B-splines, demons and the SyN method on 22 registration tasks from eleven patients. Results showed that our approach is more accurate than the contenders for liver as well as for tumor tissues. We yield average liver volume overlaps of 94.0 ± 2.7% and average surface-to-surface distances of 2.02 ± 0.87 mm and 3.55 ± 2.19 mm for liver and tumor tissue, respectively. The reported distances are close to (or even below) the slice spacing (2.5 - 3.0 mm) of our data. Our approach is also the fastest, taking 35.8 ± 12.8 s per task. CONCLUSION: The presented approach is sufficiently accurate to map information available from brachytherapy pre-planning onto interventional data. It is also reasonably fast, providing a starting point for computer-aidance during intervention.
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Artefactos , Braquiterapia/métodos , Neoplasias Hepáticas/radioterapia , Hígado/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Radioterapia Asistida por Computador/métodos , Humanos , Neoplasias Hepáticas/diagnóstico , MasculinoRESUMEN
PURPOSE: In the last decades, the increasing medical interest in magnetic resonance imaging (MRI) of the spine gave rise to a growing number of publications on computerized methods for spine analysis, covering goals such as localization and segmentation of vertebrae and intervertebral discs as well as the extraction and segmentation of the spinal canal and cord. We provide a critical systematic review to work in the field, putting focus on approaches that can be applied to different imaging sequences and settings. METHODS: Work is analysed on two levels. First, methods are reviewed in detail so that the reader understands justifications and constraints of particular work. Second, work is classified according to relevant attributes and tabulated to give an impression on recent trends. We discuss the general methodical and evaluational aspects of the work as well as challenges specific to MRI such as the lack of intensity standardization and partial volume effects. RESULTS: Methods can be condensed to a small number of optimization frameworks, e.g., graphical models, cost-minimal paths and deformable models. Works sharing the same framework mainly differentiate by the types of information, i.e., pose, geometry and appearance, that are used and by the implementation thereof. MRI-specific challenges are rarely addressed explicitly, calling into question the applicability of most methods to changing imaging sequences or settings. Most often, little attention is paid to evaluation, meaning that results lack comparability and reproducibility although publicly available data sets exist. CONCLUSION: The diversity of MRI sequences and settings still poses challenges to computerized spine analysis. Further research is necessary to implement methods that are actually applicable in practice, e.g., in clinical routine or for study purposes. Certainly, manual guidance will be necessary at some point, for instance to deal with changing subject positions. Therefore, future work should put attention to the appropriate integration of manual interaction.
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Procesamiento de Imagen Asistido por Computador/métodos , Disco Intervertebral/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Columna Vertebral/diagnóstico por imagen , Humanos , Reproducibilidad de los ResultadosRESUMEN
PURPOSE: Diagnosis of neuromuscular diseases in ultrasonography is a challenging task since experts are often unable to discriminate between healthy and pathological cases. A computer-aided diagnosis (CAD) system for skeletal muscle ultrasonography was developed and tested for myositis detection in ultrasound images of biceps brachii. METHODS: Several types of features were extracted from rectangular and polygonal image regions-of-interest (ROIs), including first-order statistics, wavelet-based features, and Haralick's features. Features were chosen that are sensitive to the change in contrast and structure for pathological ultrasound images of neuromuscular diseases. The number of features was reduced by applying different sequential feature selection strategies followed by a supervised principal component analysis. For classification, two linear approaches were investigated: Fisher's classifier and the linear support vector machine (SVM) as well as the nonlinear [Formula: see text]-nearest neighbor approach. The CAD system was benchmarked on datasets of 18 subjects, seven of which were healthy, while 11 were affected by myositis. Three expert radiologists provided pre-classification and testing interpretations. RESULTS: Leave-one-out cross-validation on the training data revealed that the linear SVM was best suited for discriminating healthy and pathological muscle tissue, achieving 85/87 % accuracy, 90 % sensitivity, and 83/85 % specificity, depending on the radiologist. CONCLUSION: A muscle ultrasonography CAD system was developed, allowing a classification of an ultrasound image by one-click positioning of rectangular ROIs with minimal user effort. The applicability of the system was demonstrated with the challenging example of myositis detection, showing highly accurate results that were robust to imprecise user input.
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Diagnóstico por Computador/métodos , Enfermedades Neuromusculares/diagnóstico por imagen , Enfermedades Neuromusculares/diagnóstico , Máquina de Vectores de Soporte , Adulto , Anciano , Anciano de 80 o más Años , Algoritmos , Automatización , Humanos , Persona de Mediana Edad , Músculo Esquelético/diagnóstico por imagen , Miositis/diagnóstico por imagen , Análisis de Componente Principal , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , UltrasonografíaRESUMEN
PURPOSE: The early detection of cerebral aneurysms plays a major role in preventing subarachnoid hemorrhage. The authors present a system to automatically detect cerebral aneurysms in multimodal 3D angiographic data sets. The authors' system is parametrizable for contrast-enhanced magnetic resonance angiography (CE-MRA), time-of-flight magnetic resonance angiography (TOF-MRA), and computed tomography angiography (CTA). METHODS: Initial volumes of interest are found by applying a multiscale sphere-enhancing filter. Several features are combined in a linear discriminant function (LDF) to distinguish between true aneurysms and false positives. The features include shape information, spatial information, and probability information. The LDF can either be parametrized by domain experts or automatically by training. Vessel segmentation is avoided as it could heavily influence the detection algorithm. RESULTS: The authors tested their method with 151 clinical angiographic data sets containing 112 aneurysms. The authors reach a sensitivity of 95% with CE-MRA data sets at an average false positive rate per data set (FPDS) of 8.2. For TOF-MRA, we achieve 95% sensitivity at 11.3 FPDS. For CTA, we reach a sensitivity of 95% at 22.8 FPDS. For all modalities, the expert parametrization led to similar or better results than the trained parametrization eliminating the need for training. 93% of aneurysms that were smaller than 5 mm were found. The authors also showed that their algorithm is capable of detecting aneurysms that were previously overlooked by radiologists. CONCLUSIONS: The authors present an automatic system to detect cerebral aneurysms in multimodal angiographic data sets. The system proved as a suitable computer-aided detection tool to help radiologists find cerebral aneurysms.
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Angiografía Cerebral/métodos , Interpretación de Imagen Asistida por Computador/métodos , Imagenología Tridimensional/métodos , Aneurisma Intracraneal/diagnóstico , Aneurisma Intracraneal/patología , Imagen Multimodal/métodos , Algoritmos , Reacciones Falso Positivas , Humanos , Modelos Lineales , Angiografía por Resonancia Magnética/métodos , Dinámicas no Lineales , Sensibilidad y Especificidad , Tomografía Computarizada por Rayos XRESUMEN
We present a fully automatic 3D segmentation method for the left ventricle (LV) in human myocardial perfusion SPECT data. This model-based approach consists of 3 phases: 1. finding the LV in the dataset, 2. extracting its approximate shape and 3. segmenting its exact contour. Finding of the LV is done by flexible pattern matching, whereas segmentation is achieved by registering an anatomical model to the functional data. This model is a new kind of stable 3D mass spring model using direction-weighted 3D contour sensors. Our approach is much faster than manual segmention, which is standard in this application up to now. By testing it on 41 LV SPECT datasets of mostly pathological data, we could show, that it is very robust and its results are comparable with those made by human experts.