Computer-assisted image-based risk analysis and planning in lung surgery - a review.

In this paper, we give an overview on current trends in computer-assisted image-based methods for risk analysis and planning in lung surgery and present our own developments with a focus on computed tomography (CT) based algorithms and applications. The methods combine heuristic, knowledge based image processing algorithms for segmentation, quantification and visualization based on CT images of the lung. Impact for lung surgery is discussed regarding risk assessment, quantitative assessment of resection strategies, and surgical guiding. In perspective, we discuss the role of deep-learning based AI methods for further improvements.

Automatic lung segmentation method for MRI-based lung perfusion studies of patients with chronic obstructive pulmonary disease.

PURPOSE: A novel fully automatic lung segmentation method for magnetic resonance (MR) images of patients with chronic obstructive pulmonary disease (COPD) is presented. The main goal of this work was to ease the tedious and time-consuming task of manual lung segmentation, which is required for region-based volumetric analysis of four-dimensional MR perfusion studies which goes beyond the analysis of small regions of interest. METHODS: The first step in the automatic algorithm is the segmentation of the lungs in morphological MR images with higher spatial resolution than corresponding perfusion MR images. Subsequently, the segmentation mask of the lungs is transferred to the perfusion images via nonlinear registration. Finally, the masks for left and right lungs are subdivided into a user-defined number of partitions. Fourteen patients with two time points resulting in 28 perfusion data sets were available for the preliminary evaluation of the developed methods. RESULTS: Resulting lung segmentation masks are compared with reference segmentations from experienced chest radiologists, as well as with total lung capacity (TLC) acquired by full-body plethysmography. TLC results were available for thirteen patients. The relevance of the presented method is indicated by an evaluation, which shows high correlation between automatically generated lung masks with corresponding ground-truth estimates. CONCLUSION: The evaluation of the developed methods indicates good accuracy and shows that automatically generated lung masks differ from expert segmentations about as much as segmentations from different experts.

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.

Relationship between pulmonary artery volumes at computed tomography and pulmonary artery pressures in patients with- and without pulmonary hypertension.

OBJECTIVES: This study was designed to determine the relationship between pulmonary artery (PA) volumes at computed tomography (CT) and PA pressures at right-sided heart catheterization in patients with and without pulmonary hypertension (PAH) to develop a noninvasive CT method of PA pressure quantification. MATERIALS AND METHODS: Sixteen patients with chronic sleep apnea syndrome underwent contrast enhanced helical CT (slice thickness 3mm; pitch 2; increment 2mm) at inspiration. Eight patients had PAH while cardiopulmonary disease has been excluded in eight other patients. Vascular volumes were determined using a 3D technique (threshold seeded vascular tracing algorithm; thresholds -600 H [lower] and 3,000 H [upper]). Right-sided heart catheterization measurements were available for linear regression analysis of PA volumes and pressures. RESULTS: Correlation between PA pressures and volumes (normalized for BMI), was high in both groups (without PAH: r=.85; with PAH .90, Pearson). Compared to elevated PA pressures in patients with pulmonary hypertension (p<.005), PA volumes also were significantly increased (p<.05) among the groups. CONCLUSIONS: High correlation was found between PA volumes and mean PA pressures in patients with- and without PAH. Significant differences in PA volumes at CT-volumetry may admit non-invasive determination of pulmonary hypertension.

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.

Informatics in radiology (infoRAD): new tools for computer assistance in thoracic CT part 2. Therapy monitoring of pulmonary metastases.

Owing to the rapid development of scanner technology, thoracic computed tomography (CT) offers new possibilities but also faces enormous challenges with respect to the quality of computer-assisted diagnosis and therapy planning. In the framework of the Virtual Institute for Computer Assistance in Clinical Radiology cooperative research project, a software application was developed to assist the radiologist in the analysis of thoracic CT data for the purpose of evaluating the response to tumor therapy. The application provides follow-up support for monitoring of tumor therapy by means of volumetric quantification of tumors and temporal registration. In addition, anatomically adequate three-dimensional visualization techniques for convenient examination of large data sets are included. 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, the results of which indicated that the application improves therapy monitoring with respect to accuracy and time required.

Informatics in radiology (infoRAD): new tools for computer assistance in thoracic CT. Part 1. Functional analysis of lungs, lung lobes, and bronchopulmonary segments.

Owing to the rapid development of scanner technology, thoracic computed tomography (CT) offers new possibilities but also faces enormous challenges with respect to the quality of computer-assisted diagnosis and therapy planning. In the framework of the Virtual Institute for Computer Assistance in Clinical Radiology cooperative research project, a prototypical software application was developed to assist the radiologist in functional analysis of thoracic CT data. By identifying the anatomic compartments of the lungs, the software application enables assessment of established functional CT parameters for each individual lung, pulmonary lobe, and pulmonary segment. Such region-based assessment allows a more localized diagnosis of lung diseases such as emphysema and more accurate estimation of regional lung function from CT data. With close cooperation between computer scientists and radiologists, the software application was tested and optimized to achieve a high degree of usability. Several clinical studies were carried out, the results of which indicated that the software application improves quantification in diagnosis, therapy planning, and therapy monitoring with respect to accuracy and time required.

A digital reference model of the human bronchial tree.

In-vitro preparations of the human lung combined with high-resolution tomography can be used to derive precise models of the human lung. To develop an abstract graph representation, specially adapted image processing algorithms were applied to segment and delineate the bronchi. The graph thus obtained contains topological information about spatial coordinates, connectivities, diameters and branching angles of 1453 bronchi up to the 17th Horsfield order. The graph was analyzed for statistical and fractal properties and was compared with current models. Results indicate a model that exhibits asymmetry and multifractal properties. This newly established reference model is an important step forward in geometrical accuracy of the bronchial tree representation that will improve both analysis of lung images in clinical imaging and the realism of functional simulations.