William F. Sensakovic

Computerized segmentation and measurement of malignant pleural mesothelioma

PURPOSE The current linear method to track tumor progression and evaluate treatment efficacy is insufficient for malignant pleural mesothelioma (MPM). A volumetric method for tumor measurement could improve the evaluation of novel treatments, but a fully manual implementation of volume measurement is too tedious and time-consuming. This manuscript presents a computerized method for the three-dimensional segmentation and volumetric analysis of MPM. METHODS The computerized MPM segmentation method segments the lung parenchyma and hemithoracic cavities to define the pleural space. Nonlinear diffusion and a k-means classifier are then implemented to identify MPM in the pleural space. A database of 31 computed tomography scans from 31 patients with pathologically confirmed MPM was retrospectively collected. Three observers independently outlined five randomly selected sections in each scan. The Jaccard similarity coefficient (J) between each of the observers and between the observer-defined and computer-defined segmentations was calculated. The computer-defined and the observer-defined segmentation areas (averaged over all observers) were both calculated for each axial section and compared using Bland-Altman plots. RESULTS The median J value among observers averaged over all sections was 0.517. The median J between the computer-defined and manual segmentations was 0.484. The difference between these values was not statistically significant. The area delineated by the computerized method demonstrated variability and bias comparable to the tumor area calculated from manual delineations. CONCLUSIONS A computerized method for segmentation and measurement of MPM was developed. This method requires minimal initialization by the user and demonstrated good agreement with manually drawn outlines and area measurements. This method will allow volumetric tracking of tumor progression and may improve the evaluation of novel MPM treatments.

Automated lung segmentation of diseased and artifact-corrupted magnetic resonance sections.

Segmentation of the lungs within magnetic resonance (MR) scans is a necessary step in the computer-based analysis of thoracic MR images. This process is often confounded by image acquisition artifacts and disease-induced morphological deformation. We have developed an automated method for lung segmentation that is insensitive to these complications. The automated method was applied to 23 thoracic MR scans (413 sections) obtained from 10 patients. Two radiologists manually outlined the lung regions in a random sample of 101 sections (n=202 lungs), and the extent to which disease or artifact confounded lung border visualization was evaluated. Accuracy of lung regions extracted by the automated segmentation method was quantified by comparison with the radiologist-defined lung regions using an area overlap measure (AOM) that ranged from 0 (disjoint lung regions) to 1 (complete overlap). The AOM between each observer and the automated method was 0.82 when averaged over all lungs. The average AOM in the lung bases, where lung segmentation is most difficult, was 0.73.

Quantitative measurement of lung reexpansion in malignant pleural mesothelioma patients undergoing pleurectomy/decortication.

RATIONALE AND OBJECTIVES Malignant pleural mesothelioma (MPM) is a neoplasm that grows circumferentially along the pleura. The tumor and concurrent pleural effusion may reduce lung function by restricting or preventing lung expansion. The purpose of this study was to provide objective evidence that pleurectomy/decortication (P/D) allows trapped lung to reexpand, quantify the reexpansion based on computed tomography (CT) scans, and investigate whether the expansion persists after surgery. MATERIALS AND METHODS A database of 12 patients demonstrating unilateral MPM was collected. Each patient underwent a presurgical CT scan, surgical debulking by P/D, and two postsurgical CT scans (at 1 and 4 months). The lung volume was measured in each scan using an automated algorithm and compared for each patient across time. RESULTS An increase in the ipsilateral postsurgical lung volume was observed for 10 of 12 patients (83%) 1 month after surgery. The median ipsilateral volume increase was 44% relative to the presurgical ipsilateral volume and 21% relative to the contralateral volume. A statistically significant change in ipsilateral lung volume was not observed between 1‑month and 4‑month postsurgical scans, implying that the volume improvement persisted months after surgery. CONCLUSIONS Debulking of MPM with P/D substantially increased the ipsilateral lung volume relative to both the presurgical ipsilateral volume and the contralateral lung volume. This improvement persisted months after surgery.

Computer-assisted staging of chronic rhinosinusitis correlates with symptoms

The Lund‐Mackay (LM) staging system for chronic rhinosinusitis (CRS) does not correlate with clinical parameters, likely due to its coarse scale. We developed a “Modified Lund Mackay” (MLM) system, which uses a three‐dimensional (3D), computerized method to quantify the volume of mucosal inflammation in the sinuses, and sought to determine whether the MLM would correlate with symptoms and disease‐specific quality of life.

Magnetic Resonance Imaging of the Lung: Automated Segmentation Methods

Identification and segmentation of structures of interest are necessary steps in the computer-based analysis of medical images. Computer-aided diagnostic (CAD) systems utilize segmentation algorithms to isolate specific structures (represented by 2D or 3D regions in an image or set of images, respectively); conversely, to remove extraneous structures that may introduce errors in the computerized analysis. This step increases both the specificity and sensitivity of the CAD system and decreases computation time by focusing analysis on smaller regions representing the structures of interest. Segmentation of the lung parenchyma is often the first step when computerized analysis focuses on the thorax. High contrast, central positioning, relatively large size in comparison to other thoracic structures, and contiguous to other critical structures (e.g., heart) render the lungs useful as both a target for primary analysis and a reliable starting point for the analysis of other thoracic structures. Segmentation of lung parenchyma in computed tomography (CT) scans is one of the most extensively researched areas in medical image processing. The low density of lung parenchyma compared with surrounding soft tissue translates into high contrast on CT, which in turn facilitates the use of several image processing techniques such as histogram thresholding, active contours, and seeded region growing. Conversely, multiple factors have limited the clinical utility of thoracic magnetic resonance imaging (MRI) and thus limited the need for lung segmentation in MR scans. Contrast and orientation of magnetic resonance scans are determined by the image acquisition protocol, and thus may require image processing methods specific to each pulse sequence and image orientation. The clinical utility of thoracic MRI is also limited by low resolution and long acquisition times that cause severe image artifacts. Recent improvements in the in-plane resolution, pulse sequences, acquisition time, and contrast media (e.g., hyperpolarized gas), however, have made MR a viable modality for thoracic imaging and have renewed interest in lung segmentation for thoracic MR applications (Eibel et al., 2003; Entwisle, 2004; Evans and Gleeson, 2004; Levin et al., 2001; Weber et al., 2004).

Discrete-space versus continuous-space lesion boundary and area definitions

Measurement of the size of anatomic regions of interest in medical images is used to diagnose disease, track growth, and evaluate response to therapy. The discrete nature of medical images allows for both continuous and discrete definitions of region boundary. These definitions may, in turn, support several methods of area calculation that give substantially different quantitative values. This study investigated several boundary definitions (e.g., continuous polygon, internal discrete, and external discrete) and area calculation methods (pixel counting and Greens theorem). These methods were applied to three separate databases: A synthetic image database, the Lung Image Database Consortium database of lung nodules and a database of adrenal gland outlines. Average percent differences in area on the order of 20% were found among the different methods applied to the clinical databases. These results support the idea that inconsistent application of region boundary definition and area calculation may substantially impact measurement accuracy.

A general method for the identification and repair of concavities in segmented medical images

The segmentation of structures of interest in medical images can result in segmentation boundaries demonstrating two types of concavities: natural and incorrect. This study introduces a generalized method for identification and repair of incorrect concavities. Previous methods are framed in terms of the generalized method and new techniques for concavity identification, α -hull, and repair, active contours, are applied. The application of α -hulls also creates a natural hierarchy on concavities that leads to more efficient computations for both the characterization and repair of concavities. The generalized method is evaluated on a database of juxtapleural nodules and compared against two other methods: convex hull and morphological closing operator. The generalized method demonstrates the best overall tradeoff between juxtapleural nodule inclusion and incorrect inclusion of non-nodule tissue from surrounding structures.

Occupational Dose and Dose Limits: Experience in a Large Multisite Hospital System

PURPOSE The Nuclear Regulatory Commission (NRC) has recently proposed changes that reduce the occupational dose limits for lens dose equivalent (LDE), embryo/fetus dose, and administrative control levels (ACLs) related to deep dose equivalent (DDE). This study collected occupational dose data from a large hospital system and determined how proposed NRC regulatory changes may affect worker and hospital workflow. METHODS Radiation badge data were collected for 1,305 workers, from between 2013 and 2014, and 180 pregnancies, from between 2009 and 2014. Median values for LDE, DDE, and embryo/fetus dose were determined. Current and proposed NRC regulations were applied, and the percentage of workers exceeding regulatory limits/ACLs was recorded. Fishers exact test was applied to determine if physicians were disproportionately affected by dose regulations. RESULTS Median doses were one to two orders of magnitude lower than current annual dose limits prescribed by the NRC. Proposed NRC regulations significantly increased the percentage of workers who exceeded limits and ACLs. Interventional radiologists, pain medicine physicians, and cardiologists working in catheter laboratories were most affected by LDE limits and DDE ACLs. Nuclear medicine technologists were most affected by embryo/fetus limits. Physicians were disproportionately affected by regulations (odds ratio 26.86; P < .0001). CONCLUSIONS Proposed NRC regulatory changes will cause a small increase in the number of workers who exceed ACLs and limits. Physicians and pregnant nuclear medicine workers are most affected and may need to alter their workloads. Practical difficulties in implementing cumulative dose tracking, and use of an LDE shielding factor, should be considered.

A modified gradient correlation filter for image segmentation: Application to airway and bowel

The segmentation of structures of interest from medical images may incorrectly include adjacent structures in the segmented image (i.e., false positives). This study introduces a family of gradient correlation filters that reduce false positives in the segmented image by comparing the segmented region gradients with a user-defined model. A gradient correlation filter was applied to a database of clinical computed tomography scans for the task of differentiating airway from lung regions and bowel from lung regions. The results were evaluated using receiver-operating characteristic analysis and demonstrated excellent results for both the airway/lung and bowel/lung classification tasks.

ACR testing of a dedicated head SPECT unit

Physics testing necessary for program accreditation is rigorously defined by the ACR. This testing is easily applied to most conventional SPECT systems based on gamma camera technology. The inSPira HD is a dedicated head SPECT system based on a rotating dual clamshell design that acquires data in a dual‐spiral geometry. The unique geometry and configuration force alterations of the standard ACR physics testing protocol. Various tests, such as intrinsic planar uniformity and/or resolution, do not apply. The Data Spectrum Deluxe Phantom used for conventional SPECT testing cannot fit in the inSPira HD scanner bore, making (currently) unapproved use of the Small Deluxe SPECT Phantom necessary. Matrix size, collimator type, scanning time, reconstruction method, and attenuation correction were all varied from the typically prescribed ACR instructions. Visible spheres, sphere contrast, visible rod groups, uniformity, and root mean square (RMS) noise were measured. The acquired SPECT images surpassed the minimum ACR requirements for both spatial resolution (9.5 mm spheres resolved) and contrast (6.4 mm rod groups resolved). Sphere contrast was generally high. Integral uniformity was 4% and RMS noise was 1.7%. Noise appeared more correlated than in images from a conventional SPECT scanner. Attenuation‐corrected images produced from direct CT scanning of the phantom and a manufacturer supplied model of the phantom demonstrated negligible differences. PACS numbers: 87.57.C‐, 87.57.uh, 87.63.lj