Separation of Left and Right Lungs Using 3-Dimensional Information of Sequential Computed Tomography Images and a Guided Dynamic Programming Algorithm

Park, Sang Cheol; Leader, Joseph K.; Tan, Jun; Lee, Guee Sang; Kim, Sung Hyun; Na, In Seop

doi:10.1097/rct.0b013e31820e4389

Cited by 11 publications

(14 citation statements)

References 20 publications

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“…In the previous methods [12][13][14][15][16], based on the 2D-slice CT images, the processes used to detect the junction region and to identify the optimal path should be iteratively conducted on every slice image; consequently, they would not guarantee the 3D continuity of a separating surface between left and right lungs. As our proposed scheme is a 3D-volumetric CT data-based method, it is able to avoid such iterations and separate multiple junctions regardless of their locations in a single instance.…”

Section: Discussionmentioning

confidence: 99%

“…Most lung separation methods developed thus far are based on 2-dimensional (2D) slice images and consist of three steps: junction region detection, the search for a separating line, and lung separation [12][13][14][15][16]. Leader et al [12] used a heuristic method to detect the junction region between the left and right lungs; they searched for a separating line by finding the largest pixel value in the junction region.…”

Section: Introductionmentioning

confidence: 99%

“…However, this method involves increased computational complexity in cases in which an area of the connected region is relatively large, as the morphological operations must be repeated many times. Park et al [16] proposed a new method based on the 3-dimensional (3D) information of sequential CT images. The location of the junction was determined by ascertaining the middle point with the shortest distance between any two points on the facing lung boundary.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Left and Right Lung Separation Using Free-Formed Surface Fitting on Volumetric CT

et al. 2014

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This study presents a completely automated method for separating the left and right lungs using free-formed surface fitting on volumetric computed tomography (CT). The left and right lungs are roughly divided using iterative 3-dimensional morphological operator and a Hessian matrix analysis. A point set traversing between the initial left and right lungs is then detected with a Euclidean distance transform to determine the optimal separating surface, which is then modeled from the point set using a free-formed surfacefitting algorithm. Subsequently, the left and right lung volumes are smoothly and directly separated using the separating surface. The performance of the proposed method was estimated by comparison with that of a human expert on 44 CT examinations. For all data sets, averages of the root mean square surface distance, maximum surface distance, and volumetric overlap error between the results of the automatic and the manual methods were 0.032 mm, 2.418 mm, and 0.017 %, respectively. Our study showed the feasibility of automatically separating the left and right lungs by identifying the 3D continuous separating surface on volumetric chest CT images.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Left and Right Lung Separation Using Free-Formed Surface Fitting on Volumetric CT

et al. 2014

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“…Finally a rolling ball algorithm with 71×71 masks is implemented to get smooth lung boundaries. The details of this algorithm have been previously reported [8]. …”

Section: (1) Lung Segmentationmentioning

confidence: 99%

Improving performance of computer-aided detection of pulmonary embolisms by incorporating a new pulmonary vascular-tree segmentation algorithm

2012

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We developed a new pulmonary vascular tree segmentation/extraction algorithm. The purpose of this study was to assess whether adding this new algorithm to our previously developed computer-aided detection (CAD) scheme of pulmonary embolism (PE) could improve the CAD performance (in particular reducing false positive detection rates). A dataset containing 12 CT examinations with 384 verified pulmonary embolism regions associated with 24 threedimensional (3-D) PE lesions was selected in this study. Our new CAD scheme includes the following image processing and feature classification steps. (1) A 3-D based region growing process followed by a rolling-ball algorithm was utilized to segment lung areas. (2) The complete pulmonary vascular trees were extracted by combining two approaches of using an intensity-based region growing to extract the larger vessels and a vessel enhancement filtering to extract the smaller vessel structures. (3) A toboggan algorithm was implemented to identify suspicious PE candidates in segmented lung or vessel area. (4) A three layer artificial neural network (ANN) with the topology 27-10-1 was developed to reduce false positive detections. (5) A k-nearest neighbor (KNN) classifier optimized by a genetic algorithm was used to compute detection scores for the PE candidates. (6) A grouping scoring method was designed to detect the final PE lesions in three dimensions. The study showed that integrating the pulmonary vascular tree extraction algorithm into the CAD scheme reduced false positive rates by 16.2%. For the case based 3D PE lesion detecting results, the integrated CAD scheme achieved 62.5% detection sensitivity with 17.1 false-positive lesions per examination.

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“…The CAD first automatically segmented and computed lung volume from each CT image slice using a multi-stage process to identify lung regions and remove all other non-lung background regions depicted on CT image slice. The accuracy and robustness of our CAD scheme in lung volume segmentation has been tested using variety of CT image databases with and without lung diseases [21][22].…”

Section: Computer-aided Detection Scheme For Detect and Quantify Copdmentioning

confidence: 99%

Evaluation of correlation between CT image features and ERCC1 protein expression in assessing lung cancer prognosis

et al. 2014

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Stage I non-small-cell lung cancers (NSCLC) usually have favorable prognosis. However, high percentage of NSCLC patients have cancer relapse after surgery. Accurately predicting cancer prognosis is important to optimally treat and manage the patients to minimize the risk of cancer relapse. Studies have shown that an excision repair crosscomplementing 1 (ERCC1) gene was a potentially useful genetic biomarker to predict prognosis of NSCLC patients. Meanwhile, studies also found that chronic obstructive pulmonary disease (COPD) was highly associated with lung cancer prognosis. In this study, we investigated and evaluated the correlations between COPD image features and ERCC1 gene expression. A database involving 106 NSCLC patients was used. Each patient had a thoracic CT examination and ERCC1 genetic test. We applied a computer-aided detection scheme to segment and quantify COPD image features. A logistic regression method and a multilayer perceptron network were applied to analyze the correlation between the computed COPD image features and ERCC1 protein expression. A multilayer perceptron network (MPN) was also developed to test performance of using COPD-related image features to predict ERCC1 protein expression. A nine feature based logistic regression analysis showed the average COPD feature values in the low and high ERCC1 protein expression groups are significantly different (p < 0.01). Using a five-fold cross validation method, the MPN yielded an area under ROC curve (AUC = 0.669±0.053) in classifying between the low and high ERCC1 expression cases. The study indicates that CT phenotype features are associated with the genetic tests, which may provide supplementary information to help improve accuracy in assessing prognosis of NSCLC patients.

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Separation of Left and Right Lungs Using 3-Dimensional Information of Sequential Computed Tomography Images and a Guided Dynamic Programming Algorithm

Cited by 11 publications

References 20 publications

Automatic Left and Right Lung Separation Using Free-Formed Surface Fitting on Volumetric CT

Automatic Left and Right Lung Separation Using Free-Formed Surface Fitting on Volumetric CT

Improving performance of computer-aided detection of pulmonary embolisms by incorporating a new pulmonary vascular-tree segmentation algorithm

Evaluation of correlation between CT image features and ERCC1 protein expression in assessing lung cancer prognosis

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