Automated Delineation of Lung Tumors in PET Images Based on Monotonicity and a Tumor-Customized Criterion

Ballangan, Cherry; Wang, Xiuying; Fulham, Michael; Eberl, Stefan; Yin, Yilong; Feng, Dagan

doi:10.1109/titb.2011.2159307

Cited by 29 publications

(22 citation statements)

References 34 publications

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“…The 3D derivative cost is a function using three-dimensional derivative features which is calculated from a tumor volume as well as the volume intensity [31], [32]. The basic idea is to characterize the tissue not only on its intensity values but also its local intensity structures.…”

Section: ) 3d Derivative Termmentioning

confidence: 99%

Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images

Xiang

Zhang

et al. 2015

IEEE Trans. on Image Process.

133

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Abstract-Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in PET images and low contrast in CT images, segmentation of tumor in PET and CT images is a challenging task. In this study, we effectively integrate the two modalities by making fully use of the superior contrast of PET images and superior spatial resolution of CT images. Random walk and graph cut method are integrated to solve the segmentation problem, in which random walk is utilized as an initialization tool to provide object seeds for graph cut segmentation on PET and CT images. The co-segmentation problem is formulated as an energy minimization problem which is solved by max-flow/min-cut method. A graph includes two sub-graphs and a special link is constructed, in which one sub-graph is for PET and another is for CT, and the special link encodes a context term which penalizes the difference of the tumor segmentation on the two modalities. To fully utilize the characteristics of PET and CT images, a novel energy representation is devised. For PET, a downhill cost and a 3D derivative cost is proposed. For CT, a shape penalty cost is integrated into the region and boundary function which helps constrain the tumor location during the segmentation. We validate our algorithm on a dataset which consists of 18 PET-CT images. The experimental results indicate the proposed method is superior to the graph cut method solely using the PET or CT is more accurate compared with the random walk method, random walk co-segmentation method, and non-improved graph-cut method.Index Terms-Image segmentation, interactive segmentation, graph cut, random walks, prior information, lung tumor, positron emission tomography (PET), computed tomography (CT).

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Section: ) 3d Derivative Termmentioning

confidence: 99%

Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images

Xiang

Zhang

et al. 2015

IEEE Trans. on Image Process.

133

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“…PET-only methods included: 1) a threshold of 40 % SUVmax on PET (referred to as RG40), 2) a threshold of 50 % SUVmax (referred to as RG50), 3) an adaptive threshold method (referred to as RGa) [23], 4) Fuzzy c-means (referred to as FCM), and (5) the tumor-customized downhill method (referred to as TCD) [24]. These methods were compared to validate the contribution of CT for tumor delineation.…”

Section: Comparison Methodsmentioning

confidence: 99%

Primary lung tumor segmentation from PET–CT volumes with spatial–topological constraint

et al. 2015

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“…Three pairs of PET studies were from three patients with 6 automatic segmented lung tumor volumes by method [11]. Five pairs of CT scans from five patients and four pairs of PET-CT scans were manually delineated by experienced radiologists for lung tumor volume.…”

Section: Clinical Datasetsmentioning

confidence: 99%

Deformable registration model with local rigidity preservation for radiation therapy of lung tumor

Zheng

Wang

Chen

et al. 2012

2012 19th IEEE International Conference on Image Processing

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Deformable registration has an important application in radiation therapy and evaluation of patient response to the treatment. However, the conventional deformable registration may introduce excessive deformation on tumor shape and volume, which thereby may lead to less accurate dose delivery on the targeted tumor and possible lesion on the surrounding healthy organs. In this paper, we proposed a deformable registration model to preserve accurate tumor information while retaining the global alignment of the temporal or multimodal biomedical images. Our experimental results on 12 clinical datasets with 15 tumors demonstrated that our deformable model was able to maintain the unique pathological features of the tumor and provide appropriate alignment and correspondence.

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Automated Delineation of Lung Tumors in PET Images Based on Monotonicity and a Tumor-Customized Criterion

Cited by 29 publications

References 34 publications

Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images

Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images

Primary lung tumor segmentation from PET–CT volumes with spatial–topological constraint

Deformable registration model with local rigidity preservation for radiation therapy of lung tumor

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