Volker Dicken

Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans

Volumetric growth assessment of pulmonary lesions is crucial to both lung cancer screening and oncological therapy monitoring. While several methods for small pulmonary nodules have previously been presented, the segmentation of larger tumors that appear frequently in oncological patients and are more likely to be complexly interconnected with lung morphology has not yet received much attention. We present a fast, automated segmentation method that is based on morphological processing and is suitable for both small and large lesions. In addition, the proposed approach addresses clinical challenges to volume assessment such as variations in imaging protocol or inspiration state by introducing a method of segmentation-based partial volume analysis (SPVA) that follows on the segmentation procedure. Accuracy and reproducibility studies were performed to evaluate the new algorithms. In vivo interobserver and interscan studies on low-dose data from eight clinical metastasis patients revealed that clinically significant volume change can be detected reliably and with negligible computation time by the presented methods. In addition, phantom studies were conducted. Based on the segmentation performed with the proposed method, the performance of the SPVA volumetry method was compared with the conventional technique on a phantom that was scanned with different dosages and reconstructed with varying parameters. Both systematic and absolute errors were shown to be reduced substantially by the SPVA method. The method was especially successful in accounting for slice thickness and reconstruction kernel variations, where the median error was more than halved in comparison to the conventional approach.

Real-Time Illustration of Vascular Structures

We present real-time vascular visualization methods, which extend on illustrative rendering techniques to particularly accentuate spatial depth and to improve the perceptive separation of important vascular properties such as branching level and supply area. The resulting visualization can and has already been used for direct projection on a patients organ in the operation theater where the varying absorption and reflection characteristics of the surface limit the use of color. The important contributions of our work are a GPU-based hatching algorithm for complex tubular structures that emphasizes shape and depth as well as GPU-accelerated shadow-like depth indicators, which enable reliable comparisons of depth distances in a static monoscopic 3D visualization. In addition, we verify the expressiveness of our illustration methods in a large, quantitative study with 160 subjects

A new approach towards simultaneous activity and attenuation reconstruction in emission tomography

In single photon emission computed tomography (SPECT) one is interested in reconstructing the activity distribution f of some radiopharmaceutical. The data gathered suffer from attenuation due to the tissue density µ. Each imaged slice incorporates noisy sample values of the nonlinear attenuated Radon transform Traditional theory for SPECT reconstruction treats µ as a known parameter. In practical applications, however, µ is not known, but either crudely estimated, determined in costly additional measurements or plainly neglected. We demonstrate that an approximation of both f and µ from SPECT data alone is feasible, leading to quantitatively more accurate SPECT images. The result is based on nonlinear Tikhonov regularization techniques for parameter estimation problems in differential equations combined with Gauss-Newton-CG minimization.

Comments on “Comparative Study With New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy”

The impact of PET on radiation therapy is held back by poor methods of defining functional volumes of interest. Many new software tools are being proposed for contouring target volumes but the different approaches are not adequately compared and their accuracy is poorly evaluated due to the illdefinition of ground truth. This paper compares the largest cohort to date of established, emerging and proposed PET contouring methods, in terms of accuracy and variability. We emphasise spatial accuracy and present a new metric that addresses the lack of unique ground truth. 30 methods are used at 13 different institutions to contour functional VOIs in clinical PET/CT and a custom-built PET phantom representing typical problems in image guided radiotherapy. Contouring methods are grouped according to algorithmic type, level of interactivity and how they exploit structural information in hybrid images. Experiments reveal benefits of high levels of user interaction, as well as simultaneous visualisation of CT images and PET gradients to guide interactive procedures. Method-wise evaluation identifies the danger of over-automation and the value of prior knowledge built into an algorithm.

OncoTREAT: a software assistant for cancer therapy monitoring

AbstractObjectCancer is one of the leading causes of death worldwide and therapy options are often associated with severe stress for the patient and high costs. Therefore, precise evaluation of therapy success is essential. Material and Methods In the framework of the VICORA research project (Virtual Institute for Computer Assistance in Clinical Radiology), a software application was developed to support the radiologist in evaluating the response to tumor therapy. The application provides follow-up support for oncological therapy monitoring by volumetric quantification of lung, liver and brain metastases as well as enlarged lymph nodes and assists the user by temporal registration of lesion positions. Results With close cooperation between computer scientists and radiologists the application was tested and optimized to achieve a high degree of usability. Several clinical studies were carried out to evaluate the robustness and reproducibility of the volumetry methods. Conclusion Automatic volumetry and segmentation allows reliable detection of tumor growth and has the potential to increase reliability and significance of monitoring tumor growth in follow-up examinations.

Fast Automated Segmentation and Reproducible Volumetry of Pulmonary Metastases in CT-Scans for Therapy Monitoring

The assessment of metastatic growth under chemotherapy belongs to the daily radiological routine and is currently performed by manual measurements of largest nodule diameters. As in lung cancer screening where 3d volumetry methods have already been developed by other groups, computer assistance would be beneficial to improve speed and reliability of growth assessment. We propose a new morphology and model based approach for the fast and reproducible volumetry of pulmonary nodules that was explicitly developed to be applicable to lung metastases which are frequently large, not necessarily spherical, and often complexly attached to vasculature and chest wall. A database of over 700 nodules from more than 50 patient CT scans from various scanners was used to test the algorithm during development. An in vivo reproducibility study was conducted concerning the volumetric analysis of 105 metastases from 8 patients that were subjected to a low dose CT scan twice within several minutes. Low median volume deviations in inter-observer (0.1%) and inter-scan (4.7%) tests and a negligible average computation time of 0.3 seconds were measured. The experiments revealed that clinically significant volume change can be detected reliably by the method.

Lung lobe segmentation by anatomy-guided 3D watershed transform

Since the lobes are mostly independent anatomic compartments of the lungs, they play a major role in diagnosis and therapy of lung diseases. The exact localization of the lobe-separating fissures in CT images often represents a non-trivial task even for experts. Therefore, a lung lobe segmentation method suitable to work robustly under clinical conditions must take advantage of additional anatomic information. Due to the absence of larger blood vessels in the vicinity of the fissures, a distance transform performed on a previously generated vessel mask allows a reliable estimation of the boundaries even in cases where the fissures themselves are invisible. To make use of image regions with visible fissures, we linearly combine the original data with the distance map. The segmentation itself is performed on the combined image using an interactive 3D watershed algorithm which allows an iterative refinement of the results. The proposed method was successfully applied to CT scans of 24 patients. Preliminary intra- and inter-observer studies conducted for one of the datasets showed a volumetric variability of well below 1%. The achieved structural decomposition of the lungs not only assists in subsequent image processing steps but also allows a more accurate prediction of lobe-specific functional parameters.

3D contour based local manual correction of tumor segmentations in CT scans

Segmentation is an essential task in medical image analysis. For example measuring tumor growth in consecutive CT scans based on the volume of the tumor requires a good segmentation. Since manual segmentation takes too much time in clinical routine automatic segmentation algorithms are typically used. However there are always cases where an automatic segmentation fails to provide an acceptable segmentation for example due to low contrast, noise or structures of the same density lying close to the lesion. These erroneous segmentation masks need to be manually corrected. We present a novel method for fast three-dimensional local manual correction of segmentation masks. The user needs to draw only one partial contour which describes the lesions actual border. This two-dimensional interaction is then transferred into 3D using a live-wire based extrapolation of the contour that is given by the user in one slice. Seed points calculated from this contour are moved to adjacent slices by a block matching algorithm. The seed points are then connected by a live-wire algorithm which ensures a segmentation that passes along the border of the lesion. After this extrapolation a morphological postprocessing is performed to generate a coherent and smooth surface corresponding to the user drawn contour as well as to the initial segmentation. An evaluation on 108 lesions by six radiologists has shown that our method is both intuitive and fast. Using our method the radiologists were able to correct 96.3% of lesion segmentations rated as insufficient to acceptable ones in a median time of 44s.

Localization and Quantification of Regional and Segmental Air Trapping in Asthma

Objective: To assess the feasibility of volumetric and densitometric software to localize and quantify signs of regional air trapping after methacholine bronchoprovocations in asthma. Methods: Eight atopic subjects with mild-to-moderate asthma using short-acting β2-agonists only, with hyperresponsiveness to methacholine, were evaluated. Low-dose baseline expiratory 16-slice multidetector computed tomography scans before and after a methacholine bronchoprovocation were acquired. MeVisPULMO3D software (Bremen, Germany) was applied to the scans, providing quantitative information on volume and density measures of the total lung and each lobe. Results: After methacholine, the expiratory scan showed a median (interquartile range) increase in volume of 534 mL (357-1279 mL), a decrease in lung density (mean and 15th percentile) of 52 Hounsfield Units (HU) (116-39 HU) and 34 HU (78-25 HU), respectively, and an increase in percentage low attenuation areas of 3% (2%-6%) for the total lung, with similar patterns in individual lung lobes. The right and left lower lung lobes showed the largest increases in air trapping, 211 mL (117-363 mL) and 229 mL (155-315 mL), respectively, versus a volume increase of 70 mL (20-249 mL), 26 mL (−16-92mL), and 91 mL (−28-241 mL) for the right upper, middle, and left upper lobes, respectively. Volume changes in the lower lobes were associated with baseline forced expiratory flow between 25% and 75% of forced vital capacity, whereas low attenuation areas changes in the lower lobes were not. Conclusions: This study suggests that multidetector computed tomography scans are able to localize and quantify regional air trapping in asthma after methacholine bronchoprovocations. Volumetric measurements of the lobes as compared to densitometric measurements are superior in detecting local air trapping in gravity-dependent areas of the lung.

Novel projection views for simplified reading of thorax CT scans with multiple pulmonary nodules.

Abstract New visualization techniques for the diagnosis of nodules in high-resolution thorax CT data from multi-slice scanners are presented. The techniques are based on segmentation of the lungs and multiple projection views as well as nonlinear reformatting of the data. In particular, the visibility of small nodules close to the pleura or mediastinum may be increased substantially.