4D lung tumor segmentation via shape prior and motion cues

won, Jung; Farhangi, M. Mehdi; Amini, Amir A.

doi:10.1109/embc.2016.7590941

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…After obtaining the warped mean shape, we perform 4D (3D + t) segmentation via graph‐cuts through five phases with shape refinement via PCA analysis . The training step was discussed in Section 2.C.…”

Section: Methodsmentioning

confidence: 99%

“…Previously, authors have proposed various approaches to segment lung nodules from CT scans. In summary, these methods can be categorized into four general approaches: thresholding methods, region growing, active contours, and graph‐cut methods . In early attempts, Reeves et al performed segmentation by applying an adaptive threshold computed for each scan to compensate for the variations between two consecutive scans in conjunction with a geometrical constraint to favor spherical shape structures.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, these methods can be categorized into four general approaches: thresholding methods, 26,28 region growing, 11,30 active contours, 12,13 and graph-cut methods. 7,32 In early attempts, Reeves et al 28 performed segmentation by applying an adaptive threshold computed for each scan to compensate for the variations between two consecutive scans in conjunction with a geometrical constraint to favor spherical shape structures. Dehmeshki et al 11 proposed a region growing approach where each neighboring voxel is added to the nodule region based on intensity contrast and distance to center of the region.…”

Section: Introductionmentioning

confidence: 99%

“…Graph‐cuts segmentation, a method adopted in this paper, is another approach to segment nodules in the lung. Cha et al revised the graph‐cuts energy function to incorporate shape prior into graph‐cuts energy function.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT

2017

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Purpose: We have developed a robust tool for performing volumetric and temporal analysis of nodules from respiratory gated four-dimensional (4D) CT. The method could prove useful in IMRT of lung cancer. Methods: We modified the conventional graph-cuts method by adding an adaptive shape prior as well as motion information within a signed distance function representation to permit more accurate and automated segmentation and tracking of lung nodules in 4D CT data. Active shape models (ASM) with signed distance function were used to capture the shape prior information, preventing unwanted surrounding tissues from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend three-dimensional (3D) segmentation to 4D by warping a prior shape model through time. The algorithm has been applied to segmentation of wellcircumscribed, vascularized, and juxtapleural lung nodules from respiratory gated CT data. Results: In all cases, 4D segmentation and tracking for five phases of high-resolution CT data took approximately 10 min on a PC workstation with AMD Phenom II and 32 GB of memory. The method was trained based on 500 breath-held 3D CT data from the LIDC data base 1 and was tested on 17 4D lung nodule CT datasets consisting of 85 volumetric frames. The validation tests resulted in an average Dice Similarity Coefficient (DSC) = 0.68 for all test data. An important by-product of the method is quantitative volume measurement from 4D CT from end-inspiration to end-expiration which will also have important diagnostic value. Conclusion: The algorithm performs robust segmentation of lung nodules from 4D CT data. Signed distance ASM provides the shape prior information which based on the iterative graph-cuts framework is adaptively refined to best fit the input data, preventing unwanted surrounding tissue from merging with the segmented object.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT

2017

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“…Examples of these four categories are shown in Figure 2. In the literature, the challenges have been addressed utilizing shape prior information and refinement [68,60,31,95,22,101,97]. The purpose of shape refinement being to keep the segmented area similar to expected shapes of nodules that are available.…”

Section: Chapter II Literature Review On Lung Nodule Segmentation 1 Lmentioning

confidence: 99%

Segmentation and classification of lung nodules from Thoracic CT scans : methods based on dictionary learning and deep convolutional neural networks.

Farhangi¹

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Volumetric analysis of respiratory gated whole lung and liver CT data with motion-constrained graph cuts segmentation

won

Farhangi

Amini

2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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The conventional graph cuts technique has been widely used for image segmentation due to its ability to find the global minimum and its ease of implementation. However, it is an intensity-based technique and as a result is limited to segmentation applications where there is significant contrast between the object and the background. We modified the conventional graph cuts method by adding shape prior and motion information. Active shape models (ASM) with signed distance functions were used to capture the shape prior information, preventing unwanted surrounding tissue from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend 3D segmentation to 4D by warping a prior shape model through time. The method has been applied to segmentation of whole lung boundary and whole liver boundary from respiratory gated CT data. 4D lung boundary segmentation in five patients, and 4D liver boundary segmentation in five patients were performed and in each case, results were compared with the results from expert-delineated ground truth. 4D segmentation for five phases of CT data took approximately ten minutes on a PC workstation with AMD Phenom II and 32GB of memory. An important by-product is quantitative whole organ volumes from respiratory gated CT from end-inspiration to end-expiration which can be determined with high accuracy.

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4D lung tumor segmentation via shape prior and motion cues

Cited by 5 publications

References 11 publications

Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT

Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT

Segmentation and classification of lung nodules from Thoracic CT scans : methods based on dictionary learning and deep convolutional neural networks.

Volumetric analysis of respiratory gated whole lung and liver CT data with motion-constrained graph cuts segmentation

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