Lung lobe modeling and segmentation with individualized surface meshes

Blaffert, Thomas; Barschdorf, Hans; Berg, Jens von; Dries, Sebastian P. M.; Franz, Astrid; Klinder, Tobias; Lorenz, Cristian; Renisch, Steffen; Wiemker, Rafael

doi:10.1117/12.770099

Cited by 10 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lobe segmentation has been used in applications such as bronchial labeling [31] and model-building [32]. Some authors extend lobar segmentation ideas to extract the pulmonary segments [33][34][35].…”

Section: Brief Summary Of Published Workmentioning

confidence: 99%

Review of automatic pulmonary lobe segmentation methods from CT

Doel¹,

Gavaghan²,

Grau³

2015

Computerized Medical Imaging and Graphics

View full text Add to dashboard Cite

Section: Brief Summary Of Published Workmentioning

confidence: 99%

Review of automatic pulmonary lobe segmentation methods from CT

Doel¹,

Gavaghan²,

Grau³

2015

Computerized Medical Imaging and Graphics

View full text Add to dashboard Cite

“…lobe assignments of nodules in the IDRI database, the success of lobe segmentation was visually assessed by counting the number of anatomically implausible segmentations. Results of lobe segmentation accuracy on a smaller test set have been published earlier [3].…”

Section: Introductionmentioning

confidence: 98%

“…The registration extrapolates and smoothes the resulting lobe boundaries. _____________________________ *thomas.blaffert@philips.com Our approach uses anatomical pre-knowledge in form of a lung surface mesh model that contains additional fissure surfaces that separate the lung lobe volumes [3]. The lobe separation is obtained by an adaptation of this mesh to a distance feature derived from segmented fissure pixels of the patient data.…”

Section: Introductionmentioning

confidence: 99%

A completely automated processing pipeline for lung and lung lobe segmentation and its application to the LIDC-IDRI data base

et al. 2010

Self Cite

View full text Add to dashboard Cite

Automated segmentation of lung lobes in thoracic CT images has relevance for various diagnostic purposes like localization of tumors within the lung or quantification of emphysema. Since emphysema is a known risk factor for lung cancer, both purposes are even related to each other. The main steps of the segmentation pipeline described in this paper are the lung detector and the lung segmentation based on a watershed algorithm, and the lung lobe segmentation based on mesh model adaptation. The segmentation procedure was applied to data sets of the data base of the Image Database Resource Initiative (IDRI) that currently contains over 500 thoracic CT scans with delineated lung nodule annotations. We visually assessed the reliability of the single segmentation steps, with a success rate of 98% for the lung detection and 90% for lung delineation. For about 20% of the cases we found the lobe segmentation not to be anatomically plausible. A modeling confidence measure is introduced that gives a quantitative indication of the segmentation quality. For a demonstration of the segmentation method we studied the correlation between emphysema score and malignancy on a per-lobe basis.

show abstract

“…The method described in Ref. 15 involves fitting an average geometrical mesh model to a fissure feature image. Results are reported on a total of 23 scans, and total execution time was reported to be approximately 90 s on a 1.4 GHz Intel Pentium CPU.…”

Section: Introductionmentioning

confidence: 99%

Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting

et al. 2013

View full text Add to dashboard Cite

Purpose: Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable. Methods: The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes. Results: The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT. Conclusions: The proposed algorithm is effective for lung lobe segmentation in absence of auxiliary structures such as vessels and airways. The most challenging cases are those with mostly incomplete, absent, or near-absent fissures and in cases with poorly revealed fissures due to high image noise.

show abstract

Lung lobe modeling and segmentation with individualized surface meshes

Cited by 10 publications

References 8 publications

Review of automatic pulmonary lobe segmentation methods from CT

Review of automatic pulmonary lobe segmentation methods from CT

A completely automated processing pipeline for lung and lung lobe segmentation and its application to the LIDC-IDRI data base

Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting

Contact Info

Product

Resources

About