Steffen Renisch

Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy

During the last couple of years virtual endoscopic systems (VES) have emerged as standard tools that are nowadays close to be utilized in daily clinical practice. Such tools render hollow human structures, allowing a clinician to visualize their inside in an endoscopic-like paradigm. It is common practice that the camera of a virtual endoscope is attached to the centerline of the structure of interest, to facilitate navigation. This centerline has to be determined manually or automatically, prior to an investigation. While there exist techniques that can straightforwardly handle simple tube-like structures (e.g. colon, aorta), structures like the tracheobronchial tree still represent a challenge due to their complex branching. In these cases it is necessary to determine all branching points within the tree which is - because of the complexity - impractical to be accomplished in a manual manner. This paper presents a simultaneous segmentation/skeletonization algorithm that extracts all major airway branches and large parts of the minor distal branches (up to 7th order) using a front propagation approach. During the segmentation the algorithm keeps track of the centerline of the segmented structure and detects all branching points. This in turn allows the full reconstruction of the tracheobronchial tree.

A General Framework for Tree Segmentation and Reconstruction from Medical Volume Data

Many anatomical entities like different parts of the arterial system and the airway system are of tree-like structure. State of the art medical imaging systems can acquire 3D volume data of the human body at a resolution that is sufficient for the visualization of these tree structures. We present a general framework for the simultaneous segmentation and reconstruction of the abovementioned entities and apply it to the extraction of coronary arteries from multi detector-row CT data. The coronary artery extraction is evaluated on 9 data-sets with known ground truth for the centerlines of the coronary arteries.

Simultaneous segmentation and tree reconstruction of the coronary arteries in MSCT images

Multislice CT angiography (MSCTA) is an emerging modality for assessing the coronary arteries. The use of MSCTA for coronary artery disease (CAD) quantification requires an assessment procedure of the coronary arteries that is automated as much as possible. We present an algorithm for the segmentation of the coronary tree with simultaneous extraction of the centerline and the tree-structure. Our approach limits the required user interaction to the placement of one landmark in the left and right main coronary artery respectively. The whole segmentation process takes about 15 s on a mid-sized PC (1GHz) including a real-time visualization of the segmentation in progress. The presented method combines a fast region expansion method (fast marching/front propagation) with heuristic reasoning. The spreading front is monitored for front-splitting enabling branch detection and simultaneous tree reconstruction of the segmented object. This approach allows for the individual treatment of tree-branches with respect to, e.g., threshold settings and reasoning on tree and sub-tree level. This approach can be applied quite generally to the segmentation of tree-like structures. The segmentation results support efficient reporting by enabling automatic generation of overview visualizations, guidance for virtual endoscopy, generation of curved MPRs along the vessels, or cross-sectional area graphs.

Modeling the coronary artery tree

The paper describes a model of the coronary artery tree. It has been developed for the context of medical image processing and medical reporting. The model combines geometric information with associated medical nomenclature and anatomical relations. Applicability of the model and interindividual geometrical variability has been studied by matching the model to the individual coronary anatomy of 33 patients imaged with a multidetector-row computer tomography scanner.

Lung lobe modeling and segmentation with individualized surface meshes

An automated segmentation of lung lobes in thoracic CT images is of interest for various diagnostic purposes like the quantification of emphysema or the localization of tumors within the lung. Although the separating lung fissures are visible in modern multi-slice CT-scanners, their contrast in the CT-image often does not separate the lobes completely. This makes it impossible to build a reliable segmentation algorithm without additional information. Our approach uses general anatomical knowledge represented in a geometrical mesh model to construct a robust lobe segmentation, which even gives reasonable estimates of lobe volumes if fissures are not visible at all. The paper describes the generation of the lung model mesh including lobes by an average volume model, its adaptation to individual patient data using a special fissure feature image, and a performance evaluation over a test data set showing an average segmentation accuracy of 1 to 3 mm.

Automatic lesion tracking for a PET/CT based computer aided cancer therapy monitoring system

Response assessment of cancer therapy is a crucial component towards a more effective and patient individualized cancer therapy. Integrated PET/CT systems provide the opportunity to combine morphologic with functional information. However, dealing simultaneously with several PET/CT scans poses a serious workflow problem. It can be a difficult and tedious task to extract response criteria based upon an integrated analysis of PET and CT images and to track these criteria over time. In order to improve the workflow for serial analysis of PET/CT scans we introduce in this paper a fast lesion tracking algorithm. We combine a global multi-resolution rigid registration algorithm with a local block matching and a local region growing algorithm. Whenever the user clicks on a lesion in the base-line PET scan the course of standardized uptake values (SUV) is automatically identified and shown to the user as a graph plot. We have validated our method by a data collection from 7 patients. Each patient underwent two or three PET/CT scans during the course of a cancer therapy. An experienced nuclear medicine physician manually measured the courses of the maximum SUVs for altogether 18 lesions. As a result we obtained that the automatic detection of the corresponding lesions resulted in SUV measurements which are nearly identical to the manually measured SUVs. Between 38 measured maximum SUVs derived from manual and automatic detected lesions we observed a correlation of 0.9994 and a average error of 0.4 SUV units.

Fast automatic delineation of cardiac volume of interest in MSCT images

Computed Tomography Angiography (CTA) is an emerging modality for assessing cardiac anatomy. The delineation of the cardiac volume of interest (VOI) is a pre-processing step for subsequent visualization or image processing. It serves the suppression of anatomic structures being not in the primary focus of the cardiac application, such as sternum, ribs, spinal column, descending aorta and pulmonary vasculature. These structures obliterate standard visualizations such as direct volume renderings or maximum intensity projections. In addition, outcome and performance of post-processing steps such as ventricle suppression, coronary artery segmentation or the detection of short and long axes of the heart can be improved. The structures being part of the cardiac VOI (coronary arteries and veins, myocardium, ventricles and atria) differ tremendously in appearance. In addition, there is no clear image feature associated with the contour (or better cut-surface) distinguishing between cardiac VOI and surrounding tissue making the automatic delineation of the cardiac VOI a difficult task. The presented approach locates in a first step chest wall and descending aorta in all image slices giving a rough estimate of the location of the heart. In a second step, a Fourier based active contour approach delineates slice-wise the border of the cardiac VOI. The algorithm has been evaluated on 41 multi-slice CT data-sets including cases with coronary stents and venous and arterial bypasses. The typical processing time amounts to 5-10s on a 1GHz P3 PC.

4DCT image-based lung motion field extraction and analysis

Respiratory motion is a complicating factor in radiation therapy, tumor ablation, and other treatments of the thorax and upper abdomen. In most cases, the treatment requires a demanding knowledge of the location of the organ under investigation. One approach to reduce the uncertainty of organ motion caused by breathing is to use prior knowledge of the breathing motion. In this work, we extract lung motion fields of seven patients in 4DCT inhale-exhale images using an iterative shape-constrained deformable model approach. Since data was acquired for radiotherapy planning, images of the same patient over different weeks of treatment were available. Although, respiratory motion shows a repetitive character, it is well-known that patients variability in breathing pattern impedes motion estimation. A detailed motion field analysis is performed in order to investigate the reproducibility of breathing motion over the weeks of treatment. For that purpose, parameters being significant for breathing motion are derived. The analysis of the extracted motion fields provides a basis for a further breathing motion prediction. Patient-specific motion models are derived by averaging the extracted motion fields of each individual patient. The obtained motion models are adapted to each patient in a leave-one-out test in order to simulate motion estimation to unseen data. By using patient-specific mean motion models 60% of the breathing motion can be captured on average.

An Adaptive Irregular Grid Approach Using SIFT Features for Elastic Medical Image Registration

Elastic image registration is an active field of current research. In contrast to B-spline transformations, defined on a regular grid of control points, we consider physics-based transformations defined on irregular grids. With such control point arrangements, the required nonlinear behaviour can be described with less parameters. We combine this transformation model with the SIFT algorithm for identifying prominent image structures that serve well as initial control point positions. Medical applications from different modalities show that with this intelligent control point initialization the number of required control points can be further reduced, which significantly speeds up the registration process.

Comparison of threshold-based and watershed-based segmentation for the truncation compensation of PET/MR images

Recently introduced combined PET/MR scanners need to handle the specific problem that a limited MR field of view sometimes truncates arm or body contours, which prevents an accurate calculation of PET attenuation correction maps. Such maps of attenuation coefficients over body structures are required for a quantitatively correct PET image reconstruction. This paper addresses this problem by presenting a method that segments a preliminary reconstruction type of PET images, time of flight non-attenuation corrected (ToF-NAC) images, and outlining a processing pipeline that compensates the arm or body truncation with this segmentation. The impact of this truncation compensation is demonstrated together with a comparison of two segmentation methods, simple gray value threshold segmentation and a watershed algorithm on a gradient image. Our results indicate that with truncation compensation a clinically tolerable quantitative SUV error is robustly achievable.