Dataset Information

Pulmonary lobar volumetry using novel volumetric computer-aided diagnosis and computed tomography.

ABSTRACT: OBJECTIVES: To compare the accuracy of pulmonary lobar volumetry using the conventional number of segments method and novel volumetric computer-aided diagnosis using 3D computed tomography images. METHODS: We acquired 50 consecutive preoperative 3D computed tomography examinations for lung tumours reconstructed at 1-mm slice thicknesses. We calculated the lobar volume and the emphysematous lobar volume < -950 HU of each lobe using (i) the slice-by-slice method (reference standard), (ii) number of segments method, and (iii) semi-automatic and (iv) automatic computer-aided diagnosis. We determined Pearson correlation coefficients between the reference standard and the three other methods for lobar volumes and emphysematous lobar volumes. We also compared the relative errors among the three measurement methods. RESULTS: Both semi-automatic and automatic computer-aided diagnosis results were more strongly correlated with the reference standard than the number of segments method. The correlation coefficients for automatic computer-aided diagnosis were slightly lower than those for semi-automatic computer-aided diagnosis because there was one outlier among 50 cases (2%) in the right upper lobe and two outliers among 50 cases (4%) in the other lobes. The number of segments method relative error was significantly greater than those for semi-automatic and automatic computer-aided diagnosis (P < 0.001). The computational time for automatic computer-aided diagnosis was 1/2 to 2/3 than that of semi-automatic computer-aided diagnosis. CONCLUSIONS: A novel lobar volumetry computer-aided diagnosis system could more precisely measure lobar volumes than the conventional number of segments method. Because semi-automatic computer-aided diagnosis and automatic computer-aided diagnosis were complementary, in clinical use, it would be more practical to first measure volumes by automatic computer-aided diagnosis, and then use semi-automatic measurements if automatic computer-aided diagnosis failed.

SUBMITTER: Iwano S

PROVIDER: S-EPMC3686391 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Pulmonary lobar volumetry using novel volumetric computer-aided diagnosis and computed tomography.

Iwano Shingo S Kitano Mariko M Matsuo Keiji K Kawakami Kenichi K Koike Wataru W Kishimoto Mariko M Inoue Tsutomu T Li Yuanzhong Y Naganawa Shinji S

Interactive cardiovascular and thoracic surgery 20130322 1

<h4>Objectives</h4>To compare the accuracy of pulmonary lobar volumetry using the conventional number of segments method and novel volumetric computer-aided diagnosis using 3D computed tomography images.<h4>Methods</h4>We acquired 50 consecutive preoperative 3D computed tomography examinations for lung tumours reconstructed at 1-mm slice thicknesses. We calculated the lobar volume and the emphysematous lobar volume < -950 HU of each lobe using (i) the slice-by-slice method (reference standard), ...[more]

PMID: 23526418

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Project description:The purpose of this work was to develop and validate a computer-aided method for the 3D segmentation of lymph nodes in CT images. The proposed method can be utilized to facilitate applications like biopsy planning, image guided radiation treatment, or assessment of response to therapy.An optimal surface finding based lymph node segmentation method was developed. Based on the approximate center point of a lymph node of interest, a graph is generated, which represents the local neighborhood around the lymph node at discrete locations (graph nodes). A cost function is calculated based on a weighted edge and region homogeneity term. By means of optimization, a surface-based segmentation of the lymph node is derived. In addition, an interactive segmentation refinement algorithm was developed, which allows the user to quickly correct segmentation errors, if needed. For assessment of segmentation accuracy, 111 lymph nodes of mediastinum, abdomen, head/neck, and axillary regions from 35 volumetric CT scans were utilized. For accuracy analysis, lymph nodes were divided into three test sets based on lymph node size and spatial resolution of the CT scan. The average lymph node size for test set I, II, and III was 1056, 1621, and 501 mm(3), respectively. Spatial resolution of test set II was lower than for test sets I and III. To generate an independent reference standard for comparison, all 111 lymph nodes were segmented by an expert with a live wire approach.All test sets were segmented with the proposed approach. Out of the 111 lymph nodes, 40 cases (36%) required computer-aided refinement of initial segmentation results. The refinement typically required 10 s per lymph node. The mean and standard deviation of the Dice coefficient for final segmentations was 0.847 ± 0.061, 0.836 ± 0.058, and 0.809 ± 0.070 for test sets I, II, and II, respectively. The average signed surface distance error was 0.023 ± 0.171, 0.394 ± 0.189, and 0.001 ± 0.146 mm for test sets I, II, and II, respectively. The time required for locating the approximate center point of a target lymph node in a scan, generating an initial OSF segmentation, and refining the segmentation, if needed, is typically less than one minute.Segmentation of lymph nodes in volumetric CT images is a challenging task due to partial volume effects, nearby strong edges, neighboring structures with similar intensity profiles and potentially inhomogeneous density of lymph nodes. The presented approach addresses many of these obstacles. In the majority of cases investigated, the initial segmentation method delivered results that did not require further processing. In addition, the computer-aided segmentation refinement framework was found to be effective in dealing with potentially occurring segmentation errors.

Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) and pleuroparenchymal fibroelastosis (PPFE) are known to have poor outcomes but detailed examinations of prognostic significance of an association between these morphologic processes are lacking.MethodsRetrospective observational study of independent derivation and validation cohorts of IPF populations. Upper-lobe PPFE extent was scored visually (vPPFE) as categories of absent, moderate, marked. Computerised upper-zone PPFE extent (cPPFE) was examined continuously and using a threshold of 2·5% pleural surface area. vPPFE and cPPFE were evaluated against 1-year FVC decline (estimated using mixed-effects models) and mortality. Multivariable models were adjusted for age, gender, smoking history, antifibrotic treatment and diffusion capacity for carbon monoxide.FindingsPPFE prevalence was 49% (derivation cohort, n = 142) and 72% (validation cohort, n = 145). vPPFE marginally contributed 3-14% to variance in interstitial lung disease (ILD) severity across both cohorts.In multivariable models, marked vPPFE was independently associated with 1-year FVC decline (derivation: regression coefficient 18·3, 95 CI 8·47-28·2%; validation: 7·51, 1·85-13·2%) and mortality (derivation: hazard ratio [HR] 7·70, 95% CI 3·50-16·9; validation: HR 3·01, 1·33-6·81). Similarly, continuous and dichotomised cPPFE were associated with 1-year FVC decline and mortality (cPPFE ≥ 2·5% derivation: HR 5·26, 3·00-9·22; validation: HR 2·06, 1·28-3·31). Individuals with cPPFE ≥ 2·5% or marked vPPFE had the lowest median survival, the cPPFE threshold demonstrated greater discrimination of poor outcomes at two and three years than marked vPPFE.InterpretationPPFE quantification supports distinction of IPF patients with a worse outcome independent of established ILD severity measures. This has the potential to improve prognostic management and elucidate separate pathways of disease progression.FundingThis research was funded in whole or in part by the Wellcome Trust [209,553/Z/17/Z] and the NIHR UCLH Biomedical Research Centre, UK.

Dataset Information

Pulmonary lobar volumetry using novel volumetric computer-aided diagnosis and computed tomography.

Publications

Pulmonary lobar volumetry using novel volumetric computer-aided diagnosis and computed tomography.

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