Stefan Krass

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.

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.

Reproducibility of airway wall thickness measurements

Airway remodeling and accompanying changes in wall thickness are known to be a major symptom of chronic obstructive pulmonary disease (COPD), associated with reduced lung function in diseased individuals. Further investigation of this disease as well as monitoring of disease progression and treatment effect demand for accurate and reproducible assessment of airway wall thickness in CT datasets. With wall thicknesses in the sub-millimeter range, this task remains challenging even with todays high resolution CT datasets. To provide accurate measurements, taking partial volume effects into account is mandatory. The Full-Width-at-Half-Maximum (FWHM) method has been shown to be inappropriate for small airways1,2 and several improved algorithms for objective quantification of airway wall thickness have been proposed.1-8 In this paper, we describe an algorithm based on a closed form solution proposed by Weinheimer et al.7 We locally estimate the lung density parameter required for the closed form solution to account for possible variations of parenchyma density between different lung regions, inspiration states and contrast agent concentrations. The general accuracy of the algorithm is evaluated using basic tubular software and hardware phantoms. Furthermore, we present results on the reproducibility of the algorithm with respect to clinical CT scans, varying reconstruction kernels, and repeated acquisitions, which is crucial for longitudinal observations.

Automatic segmentation of lung lobes in ct images based on fissures, vessels, and bronchi

Lobewise analysis of the pulmonary parenchyma is of clinical relevance for diagnosing and monitoring pathologies. In this work, a fully automatic lobe segmentation approach is presented, which is based on a previously proposed watershed transformation approach. The proposed extension explicitly considers the pulmonary fissures by including them in the cost image for the watershed segmentation. The fissure structures are found through a tailored feature analysis of the Hessian matrix. The method is evaluated using 42 data sets, and a comparison with manual segmentations yields an average volumetric agreement of 96.8%. In comparison to the previously proposed approach, this method increases segmentation accuracy where the fissures are visible.

[Three-dimensional reconstruction of central lung tumors based on CT data].

BACKGROUND CT scanning of the lungs is the standard procedure for preoperative evaluation of central lung tumors. The extent of the tumor and infiltration of central lung structures or lung segments are decisive parameters to clarify whether surgery is possible and the extent of resection. With computer-assisted methods for the segmentation of anatomical structures based on CT data (Fraunhofer MeVis, Bremen) an enhanced, three-dimensional selective visualization is now possible. PATIENTS AND METHODS From August 2007 through June 2009, 22 patients with central lung tumors were treated at the department of thoracic surgery, University of Schleswig-Holstein, campus Lübeck. There were 15 males and 7 females with a mean age of 60.2 years (range 41-74 years), 18 patients had a long history of smoking, while 4 patients had never smoked. Of the patients 20 had a primary lung carcinoma, 1 patient had local recurrent lung cancer after lobectomy and 1 patient had a central lung metastasis from a non-pulmonary primary carcinoma. A multi-slice detector computer tomogram (MSDCT) scan was performed in all cases. All data were three-dimensionally reconstructed and visualized using special computer-aided software (Fraunhofer MeVis, Bremen). Pulmonary lung function tests, computed postoperative lung volume, bronchoscopic findings, general condition of the patients and the three-dimensionally reconstructed CT data were used for an individual risk analysis and surgical planning. RESULTS According to the risk analysis 14 out of the 22 patients were surgically treated, 7 patients were staged as functionally inoperable and 1 as technically inoperable. A pneumonectomy was performed in 5 cases, a lobectomy/bilobectomy in 4 cases, an extended lobectomy in 3 cases and 1 case each of a wedge resection and a sleeve resection. Of the 14 patients 2 were classified as stage Ia/b, 7 patients as stage IIa/b and 5 patients as stage IIIa. The median length of time spent in hospital was 8.5±33 days and the mortality rate was 0%. The three-dimensional visualization of the tumor and its anatomical relationship to central pulmonary vessels and the airway system was feasible in all cases. The three-dimensional reconstruction was confirmed in all cases by surgical exploration. CONCLUSION Three-dimensional reconstruction of CT scan data is a new and promising method for preoperative presentation and risk analysis of central lung tumors. The three-dimensional visualization with anatomical reformatting and color-coded segmentation enables the surgeon to make a more precise strategic approach for central lung tumors.

Dreidimensionale Rekonstruktion von zentralen Lungentumoren basierend auf CT-Daten

BACKGROUND CT scanning of the lungs is the standard procedure for preoperative evaluation of central lung tumors. The extent of the tumor and infiltration of central lung structures or lung segments are decisive parameters to clarify whether surgery is possible and the extent of resection. With computer-assisted methods for the segmentation of anatomical structures based on CT data (Fraunhofer MeVis, Bremen) an enhanced, three-dimensional selective visualization is now possible. PATIENTS AND METHODS From August 2007 through June 2009, 22 patients with central lung tumors were treated at the department of thoracic surgery, University of Schleswig-Holstein, campus Lübeck. There were 15 males and 7 females with a mean age of 60.2 years (range 41-74 years), 18 patients had a long history of smoking, while 4 patients had never smoked. Of the patients 20 had a primary lung carcinoma, 1 patient had local recurrent lung cancer after lobectomy and 1 patient had a central lung metastasis from a non-pulmonary primary carcinoma. A multi-slice detector computer tomogram (MSDCT) scan was performed in all cases. All data were three-dimensionally reconstructed and visualized using special computer-aided software (Fraunhofer MeVis, Bremen). Pulmonary lung function tests, computed postoperative lung volume, bronchoscopic findings, general condition of the patients and the three-dimensionally reconstructed CT data were used for an individual risk analysis and surgical planning. RESULTS According to the risk analysis 14 out of the 22 patients were surgically treated, 7 patients were staged as functionally inoperable and 1 as technically inoperable. A pneumonectomy was performed in 5 cases, a lobectomy/bilobectomy in 4 cases, an extended lobectomy in 3 cases and 1 case each of a wedge resection and a sleeve resection. Of the 14 patients 2 were classified as stage Ia/b, 7 patients as stage IIa/b and 5 patients as stage IIIa. The median length of time spent in hospital was 8.5±33 days and the mortality rate was 0%. The three-dimensional visualization of the tumor and its anatomical relationship to central pulmonary vessels and the airway system was feasible in all cases. The three-dimensional reconstruction was confirmed in all cases by surgical exploration. CONCLUSION Three-dimensional reconstruction of CT scan data is a new and promising method for preoperative presentation and risk analysis of central lung tumors. The three-dimensional visualization with anatomical reformatting and color-coded segmentation enables the surgeon to make a more precise strategic approach for central lung tumors.

Segmentbestimmung im Computertomogramm der Lunge In-vitro Validierung

Fur die radiologische Diagnostik ist die Kenntnis der Lungenlappensegmente zur segmentgenauen Berechnung von CT-Funktionsparametern und zur Tumorlokalisation ein Gewinn. Nach Vorverarbeitung der computertomographischen Bilddaten der Lunge wird der Bronchialbaum mit einem speziellen Bereichswachstumsverfahren segmentiert und automatisch in seine Unterbaume zerlegt. Ein auf Wachstumsmodellen basierender Algorithmus approximiert daraus die Grenzen der Lungenlappensegmente. Die Validierung mit zwei in-vitro Praparaten der Lunge ergab fur klinische HRCT-Daten eine Genauigkeit der Segmentapproximation von ca. 70%.

Determination of lung segments in computed tomography images using the Euclidean distance to the pulmonary artery

PURPOSE Computed tomography (CT) imaging is the modality of choice for lung cancer diagnostics. With the increasing number of lung interventions on sublobar level in recent years, determining and visualizing pulmonary segments in CT images and, in oncological cases, reliable segment-related information about the location of tumors has become increasingly desirable. Computer-assisted identification of lung segments in CT images is subject of this work. METHODS The authors present a new interactive approach for the segmentation of lung segments that uses the Euclidean distance of each point in the lung to the segmental branches of the pulmonary artery. The aim is to analyze the potential of the method. Detailed manual pulmonary artery segmentations are used to achieve the best possible segment approximation results. A detailed description of the method and its evaluation on 11 CT scans from clinical routine are given. RESULTS An accuracy of 2-3 mm is measured for the segment boundaries computed by the pulmonary artery-based method. On average, maximum deviations of 8 mm are observed. 135 intersegmental pulmonary veins detected in the 11 test CT scans serve as reference data. Furthermore, a comparison of the presented pulmonary artery-based approach to a similar approach that uses the Euclidean distance to the segmental branches of the bronchial tree is presented. It shows a significantly higher accuracy for the pulmonary artery-based approach in lung regions at least 30 mm distal to the lung hilum. CONCLUSIONS A pulmonary artery-based determination of lung segments in CT images is promising. In the tests, the pulmonary artery-based determination has been shown to be superior to the bronchial tree-based determination. The suitability of the segment approximation method for application in the planning of segment resections in clinical practice has already been verified in experimental cases. However, automation of the method accompanied by an evaluation on a larger number of test cases is required before application in the daily clinical routine.