System for definition of the central-chest vasculature

Taeprasartsit, Pinyo; Higgins, William E.

doi:10.1117/12.811143

Cited by 4 publications

(5 citation statements)

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“…To this end, we have used the LNSM to perform a detailed quantitative study of the lymph nodes depicted in our database [38,45, 47]. In addition, we have performed studies to determine the ability of both radiologists and pulmonologists to identify lymph nodes in MDCT, and we have made early efforts to apply the LNSM to planning follow-on staging bronchoscopy [24,48–50]. …”

Section: Discussionmentioning

confidence: 99%

“…In particular, for the set (1), we compute the segmented airway tree using the methods of [22,25], we compute the three sections of the aorta, aorta centerline, and PA using the methods of [27,28], and methods for segmenting the lungs, sternum, and spine appear in [22,24]. References [20,21] describe the methods for airway endoluminal surface definition, while [29] describes the centerline method.…”

Section: Methodsmentioning

confidence: 99%

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Automatic definition of the central-chest lymph-node stations

et al. 2011

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Purpose Lung cancer remains the leading cause of cancer death in the United States. Central to the lung-cancer diagnosis and staging process is the assessment of the central-chest lymph nodes. This assessment requires two steps: (1) examination of the lymph-node stations and identification of diagnostically important nodes in a three-dimensional (3D) multidetector computed tomography (MDCT) chest scan; (2) tissue sampling of the identified nodes. We describe a computer-based system for automatically defining the central-chest lymph-node stations in a 3D MDCT chest scan. Methods Automated methods first construct a 3D chest model, consisting of the airway tree, aorta, pulmonary artery, and other anatomical structures. Subsequent automated analysis then defines the 3D regional nodal stations, as specified by the internationally standardized TNM lung-cancer staging system. This analysis involves extracting over 140 pertinent anatomical landmarks from structures contained in the 3D chest model. Next, the physician uses data mining tools within the system to interactively select diagnostically important lymph nodes contained in the regional nodal stations. Results Results from a ground-truth database of unlabeled lymph nodes identified in 32 MDCT scans verify the system’s performance. The system automatically defined 3D regional nodal stations that correctly labeled 96% of the database’s lymph nodes, with 93% of the stations correctly labeling 100% of their constituent nodes. Conclusions The system accurately defines the regional nodal stations in a given high-resolution 3D MDCT chest scan and eases a physician’s burden for analyzing a given MDCT scan for lymph-node station assessment. It also shows potential as an aid for preplanning lung-cancer staging procedures.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automatic definition of the central-chest lymph-node stations

et al. 2011

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“…penalize high bending (note that curvature is the reciprocal of the radius of curvature), with r min being the minimum pulmonary artery radius found during Hough transform, as it is impossible for the pulmonary artery to have a smaller radius of curvature than its own radius. Compared to previous work, 2,11,15 where one or more models need to be fitted globally as well as locally using several energy terms, we here greatly reduce the amount and complexity of optimizations to a single local minimization, making the approach faster and stable.…”

Section: Nurbs Fittingmentioning

confidence: 99%

“…five to eight user selected landmarks 12 or a manually placed seed point 13 ), or fail for up to 20% of cases, 2,11 as the utilized fixed model or models are not flexible enough. We here present the first work that addresses these problems: Using an adaptive model, it works equally well on a large number of contrast enhanced and non-contrast data sets.…”

Section: Related Workmentioning

confidence: 99%

Adaptive model based pulmonary artery segmentation in 3D chest CT

2010

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The extraction and analysis of the pulmonary artery in computed tomography (CT) of the chest can be an important, but time-consuming step for the diagnosis and treatment of lung disease, in particular in non-contrast data, where the pulmonary artery has low contrast and frequently merges with adjacent tissue of similar intensity. We here present a new method for the automatic segmentation of the pulmonary artery based on an adaptive model, Hough and Euclidean distance transforms, and spline fitting, which works equally well on non-contrast and contrast enhanced data. An evaluation on 40 patient data sets and a comparison to manual segmentations in terms of Jaccard index, sensitivity, specificity, and minimum mean distance shows its overall robustness.

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“…The publications can be mainly categorized into detection [12,13,14,15,16] and segmentation [17,18,19,20,21,22,23,24] of lymph nodes, labeling of lymph node stations [25,26], and computer assisted treatment planning [27]. For the segmentation of lymph nodes, recently also a commercial system has been announced [28].…”

Section: Introductionmentioning

confidence: 99%

Mediastinal atlas creation from 3-D chest computed tomography images: Application to automated detection and station mapping of lymph nodes

Feuerstein

Glocker

Kitasaka

et al. 2012

Medical Image Analysis

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One important aspect of lung cancer staging is the assessment of mediastinal lymph nodes in 3-D chest computed tomography (CT) images. In the current clinical routine this is done manually by analyzing the 3-D CT image slice by slice to find nodes, evaluate them quantitatively, and assign labels to them for describing the clinical and pathologic extent of metastases. In this paper we present a method to automate the process of lymph node detection and labeling by creation of a mediastinal average image and a novel lymph node atlas containing probability maps for mediastinal, aortic, and N1 nodes. Utilizing a fast deformable registration approach to match the atlas with CT images of new patients, our method can maintain an acceptable runtime. In comparison to previously published methods for mediastinal lymph node detection and labeling it also shows a good sensitivity and positive predictive value.

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System for definition of the central-chest vasculature

Cited by 4 publications

References 0 publications

Automatic definition of the central-chest lymph-node stations

Automatic definition of the central-chest lymph-node stations

Adaptive model based pulmonary artery segmentation in 3D chest CT

Mediastinal atlas creation from 3-D chest computed tomography images: Application to automated detection and station mapping of lymph nodes

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