Lobe-based quantification of tomographic images is of increasing interest for diagnosis and monitoring lung
pathology. With modern tomography scanners providing data sets with hundreds of slices, manual segmentation
is time-consuming and not feasible in the clinical routine. Especially for patients with severe lung pathology that
are of particular clinical importance, automatic segmentation approaches frequently generate partially inaccurate
or even completely unacceptable results. In this work we present a modality-independent, semi-automated
method that can be used both for generic correction of any existing lung lobe segmentation and for segmentation
from scratch. Intuitive slice-based drawing of fissure parts is used to introduce user knowledge. Internally, the
current fissure is represented as sampling points in 3D space that are interpolated to a fissure surface. Using
morphological processing, a 3D impact region is computed for each user-drawn 2D curve. Based on the curve
and impact region, the updated lobar boundary surface is immediately computed after each interaction step to
provide instant user feedback. The method was evaluated on 25 normal-dose CT scans with a reference standard
provided by a human observer. When segmenting from scratch, the average distance to the reference standard
was 1.6mm using an average of five interactions and 50 seconds of interaction time per case. When correcting
inadequate automatic segmentations, the initial error was reduced from 13.9 to 1.9mm with comparable efforts.
The evaluation shows that both correction of a given segmentation and segmentation from scratch can be
successfully performed with little interaction in a short amount of time.
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 today's 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 airways<sup>1,2</sup> and several improved algorithms for objective quantification of airway wall thickness have been proposed.<sup>1-8</sup> In this paper, we describe an algorithm based on a closed form solution proposed by Weinheimer et al.<sup>7</sup> 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.
This study was aimed to evaluate a morphology-based approach for prediction of postoperative forced expiratory volume in one second (FEV1) after lung resection from preoperative CT scans. Fifteen Patients with surgically treated (lobectomy or pneumonectomy) bronchogenic carcinoma were enrolled in the study. A preoperative chest CT and pulmonary function tests before and after surgery were performed. CT scans were analyzed by prototype software: automated segmentation and volumetry of lung lobes was performed with minimal user interaction. Determined volumes of different lung lobes were used to predict postoperative FEV<sub>1</sub> as percentage of the preoperative values. Predicted FEV<sub>1</sub> values were compared to the observed postoperative values as standard of reference. Patients underwent lobectomy in twelve cases (6 upper lobes; 1 middle lobe; 5 lower lobes; 6 right side; 6 left side) and pneumonectomy in three cases. Automated calculation of predicted postoperative lung function was successful in all cases. Predicted FEV<sub>1</sub> ranged from 54% to 95% (mean 75% ± 11%) of the preoperative values. Two cases with obviously erroneous LFT were excluded from analysis. Mean error of predicted FEV<sub>1</sub> was 20 ± 160 ml, indicating absence of systematic error; mean absolute error was 7.4 ± 3.3% respective 137 ± 77 ml/s. The 200 ml reproducibility criterion for FEV<sub>1</sub> was met in 11 of 13 cases (85%). In conclusion, software-assisted prediction of postoperative lung function yielded a clinically acceptable agreement with the observed postoperative values. This method might add useful information for evaluation of functional operability of patients with lung cancer.
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.