Matthew S. Brown

Method for segmenting chest CT image data using an anatomical model: preliminary results

Presents an automated, knowledge-based method for segmenting chest computed tomography (CT) datasets. Anatomical knowledge including expected volume, shape, relative position, and X-ray attenuation of organs provides feature constraints that guide the segmentation process. Knowledge is represented at a high level using an explicit anatomical model. The model is stored in a frame-based semantic network and anatomical variability is incorporated using fuzzy sets. A blackboard architecture permits the data representation and processing algorithms in the model domain to be independent of those in the image domain. Knowledge-constrained segmentation routines extract contiguous three-dimensional (3-D) sets of voxels, and their feature-space representations are posted on the blackboard. An inference engine uses fuzzy logic to match image to model objects based on the feature constraints. Strict separation of model and image domains allows for systematic extension of the knowledge base. In preliminary experiments, the method has been applied to a small number of thoracic CT datasets. Based on subjective visual assessment by experienced thoracic radiologists, basic anatomic structures such as the lungs, central tracheobronchial tree, chest wall, and mediastinum were successfully segmented. To demonstrate the extensibility of the system, knowledge was added to represent the more complex anatomy of lung lesions in contact with vessels or the chest wall. Visual inspection of these segmented lesions was also favorable. These preliminary results suggest that use of expert knowledge provides an increased level of automation compared with low-level segmentation techniques. Moreover, the knowledge-based approach may better discriminate between structures of similar attenuation and anatomic contiguity. Further validation is required.

Recurrent Glioblastoma Multiforme: ADC Histogram Analysis Predicts Response to Bevacizumab Treatment

PURPOSE To determine if apparent diffusion coefficient (ADC) histogram analysis can stratify progression-free survival in patients with recurrent glioblastoma multiforme (GBM) prior to bevacizumab treatment. MATERIALS AND METHODS The study was approved by the institutional review board and was HIPAA compliant; informed consent was obtained. Bevacizumab-treated and control patients (41 per cohort) diagnosed with recurrent GBM were analyzed by using whole enhancing tumor ADC histograms with a two normal distribution mixture fitting curve on baseline (pretreatment) magnetic resonance (MR) images to generate ADC classifiers, including the overall mean ADC as well as the mean ADC from the lower curve (ADC(L)). Overall and 6-month progression-free survival (as defined by the Macdonald criteria) was determined by using Cox proportional hazard ratios and the Kaplan-Meier method with log-rank test. RESULTS For bevacizumab-treated patients, the hazard ratio for progression by 6 months in patients with less than versus greater than mean ADC(L) was 4.1 (95% confidence interval: 1.6, 10.4), and there was a 2.75-fold reduction in the median time to progression. For the control patients, there was no significant difference in median time to progression for the patients with low versus high ADC(L) (hazard ratio, 1.8; 95% confidence interval: 0.9, 3.7). For bevacizumab-treated patients, pretreatment ADC more accurately stratified 6-month progression-free survival than did change in enhancing tumor volume at first follow-up (73% vs 58% accuracy, P = .034). CONCLUSION Pretreatment ADC histogram analysis can stratify progression-free survival in bevacizumab-treated patients with recurrent GBM.

Patient-specific models for lung nodule detection and surveillance in CT images

The purpose of this work is to develop patient-specific models for automatically detecting lung nodules in computed tomography (CT) images. It is motivated by significant developments in CT scanner technology and the burden that lung cancer screening and surveillance imposes on radiologists. We propose a new method that uses a patients baseline image data to assist in the segmentation of subsequent images so that changes in size and/or shape of nodules can be measured automatically. The system uses a generic, a priori model to detect candidate nodules on the baseline scan of a previously unseen patient. A user then confirms or rejects nodule candidates to establish baseline results. For analysis of follow-up scans of that particular patient, a patient-specific model is derived from these baseline results. This model describes expected features (location, volume and shape) of previously segmented nodules so that the system can relocalize them automatically on follow-up. On the baseline scans of 17 subjects, a radiologist identified a total of 36 nodules, of which 31 (86%) were detected automatically by the system with an average of 11 false positives (FPs) per case. In follow-up scans 27 of the 31 nodules were still present and, using patient-specific models, 22 (81%) were correctly relocalized by the system. The system automatically detected 16 out of a possible 20 (80%) of new nodules on follow-up scans with ten FPs per case.

Patterns of progression in patients with recurrent glioblastoma treated with bevacizumab

Objective: We evaluated patterns of tumor progression in patients with recurrent glioblastoma who were treated with bevacizumab (BEV) alone or in combination with irinotecan (CPT-11) while participating in the BRAIN study. Methods: An independent neuroradiologist reviewed MRI scans at baseline and progression in patients who received BEV (n = 85) or BEV+CPT-11 (n = 82) while on BRAIN. Tumor patterns were scored as local, distant, diffuse, or multifocal. Median progression-free survival (PFS) and overall survival (OS) were estimated using Kaplan-Meier methods. Hazard ratios for PFS and OS were estimated using a Cox regression model. Results: Twenty-eight percent of patients who participated in BRAIN had nonlocal disease at baseline (72% local disease). Sixty-seven (79%) patients treated with single-agent BEV and 57 (70%) patients treated with BEV+CPT-11 experienced disease progression while on BRAIN. Most patients in each treatment group did not have a change in the radiographic pattern of their tumor (i.e., “no shift”) at the time of progression. The proportion of BEV patients with no shift (82%) was greater than that of BEV+CPT-11 patients (53%, χ2 p = 0.0004), and a greater proportion of BEV+CPT-11 patients (39%) compared with BEV patients (16%) experienced local-to-diffuse tumor pattern at progression (χ2 p = 0.002). Patients treated with BEV or BEV+CPT-11 who had local-to-local or local-to-diffuse progression patterns had similar efficacy outcomes, including objective response, PFS, and OS. Conclusions: Most patients treated with BEV or BEV+CPT-11 on BRAIN did not experience a change from baseline in radiographic characteristics of disease at the time of progression.

Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images.

We present a knowledge-based approach to segmentation and analysis of the lung boundaries in chest X-rays. Image edges are matched to an anatomical model of the lung boundary using parametric features. A modular system architecture was developed which incorporates the model, image processing routines, an inference engine and a blackboard. Edges associated with the lung boundary are automatically identified and abnormal features are reported. In preliminary testing on 14 images for a set of 18 detectable abnormalities, the system showed a sensitivity of 88% and a specificity of 95% when compared with assessment by an experienced radiologist.

Knowledge-based segmentation of thoracic computed tomography images for assessment of split lung function.

The assessment of differential left and right lung function is important for patients under consideration for lung resection procedures such as single lung transplantation. We developed an automated, knowledge-based segmentation algorithm for purposes of deriving functional information from dynamic computed tomography (CT) image data. Median lung attenuation (HU) and area measurements were automatically calculated for each lung from thoracic CT images acquired during a forced expiratory maneuver as indicators of the amount and rate of airflow. The accuracy of these derived measures from fully automated segmentation was validated against those from segmentation using manual editing by an expert observer. A total of 1313 axial images were analyzed from 49 patients. The images were segmented using our knowledge-based system that identifies the chest wall, mediastinum, trachea, large airways and lung parenchyma on CT images. The key components of the system are an anatomical model, an inference engine and image processing routines, and segmentation involves matching objects extracted from the image to anatomical objects described in the model. The segmentation results from all images were inspected by the expert observer. Manual editing was required to correct 183 (13.94%) of the images, and the sensitivity, specificity, and accuracy of the knowledge-based segmentation were greater than 98.55% in classifying pixels as lung or nonlung. There was no significant difference between median lung attenuation or area values from automated and edited segmentations (p > 0.70). Using the knowledge-based segmentation method we can automatically derive indirect quantitative measures of single lung function that cannot be obtained using conventional pulmonary function tests.

Automated classification of lung bronchovascular anatomy in CT using AdaBoost

Lung CAD systems require the ability to classify a variety of pulmonary structures as part of the diagnostic process. The purpose of this work was to develop a methodology for fully automated voxel-by-voxel classification of airways, fissures, nodules, and vessels from chest CT images using a single feature set and classification method. Twenty-nine thin section CT scans were obtained from the Lung Image Database Consortium (LIDC). Multiple radiologists labeled voxels corresponding to the following structures: airways (trachea to 6th generation), major and minor lobar fissures, nodules, and vessels (hilum to peripheral), and normal lung parenchyma. The labeled data was used in conjunction with a supervised machine learning approach (AdaBoost) to train a set of ensemble classifiers. Each ensemble classifier was trained to detect voxels part of a specific structure (either airway, fissure, nodule, vessel, or parenchyma). The feature set consisted of voxel attenuation and a small number of features based on the eigenvalues of the Hessian matrix (used to differentiate structures by shape). When each ensemble classifier was composed of 20 weak classifiers, the AUC values for the airway, fissure, nodule, vessel, and parenchyma classifiers were 0.984+/-0.011, 0.949+/-0.009, 0.945+/-0.018, 0.953+/-0.016, and 0.931+/-0.015, respectively. The strong results suggest that this could be an effective input to higher-level anatomical based segmentation models with the potential to improve CAD performance.

Automated measurement of single and total lung volume from CT

PURPOSE The goal of this work was to develop an automated method for calculating single (SLV) and total (TLV) lung volumes from CT images. METHOD Patients underwent volumetric CT scanning through the entire chest in a single breath-hold, as well as pulmonary function tests. An automated, knowledge-based system was developed to segment the lungs in the CT images. Image-processing routines were used to extract sets of voxels from the image data that were identified by matching them to anatomical objects defined in a model. SLV and TLV were calculated by summing included voxels. RESULTS For 43 patients analyzed, TLV from CT and total lung capacity from body plethysmography were strongly correlated (r = 0.90). On average, the CT-derived volume of the left lung accounted for 47.2% of the total. CONCLUSION A knowledge-based approach to segmentation of the lungs in CT can be used to automatically estimate SLV and TLV.

Computer-aided diagnosis in lung nodule assessment.

Computed tomography (CT) imaging is playing an increasingly important role in cancer detection, diagnosis, and lesion characterization, and it is the most sensitive test for lung nodule detection. Interpretation of lung nodules involves characterization and integration of clinical and other imaging information. Advances in lung nodule management using CT require optimization of CT data acquisition, postprocessing tools, and computer-aided diagnosis (CAD). The goal of CAD systems being developed is to both assist radiologists in the more sensitive detection of nodules and noninvasively differentiate benign from malignant lesions; the latter is important given that malignant lesions account for between 1% and 11% of pulmonary nodules. The aim of this review is to summarize the current state of the art regarding CAD techniques for the detection and characterization of solitary pulmonary nodules and their potential applications in the clinical workup of these lesions.

Diagnostic performance comparison of the Chartis System and high-resolution computerized tomography fissure analysis for planning endoscopic lung volume reduction

Endobronchial valve (EBV) therapy is optimized in patients who demonstrate little or no collateral ventilation (CV). The accuracy of the Chartis System and visual assessment of high‐resolution computerized tomography (HRCT) fissure completeness by a core radiology laboratory for classifying CV status was compared by evaluating the relationship of each method with target lobe volume reduction (TLVR) after EBV placement.