Lung tumor segmentation in PET images using graph cuts

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

doi:10.1016/j.cmpb.2012.10.009

Cited by 38 publications

(24 citation statements)

References 23 publications

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“…In the second approach, known as the continuous convex relaxation method, the image is treated in a continuous domain, and the optimal labeling minimization problem, initially nonconvex, is relaxed to obtain an equivalent convex minimization problem, which is solved via continuous max-flow formulation. The scientific literature shows how these types of mathematical models have been used to segment different tumors, mainly in the lungs [4,26,27,42,44,48], liver [29], lymph nodes [5,13,16,17,54], prostate [20,25,31], brain [12] and breast [49]. Some studies used level-set and active contour methods [13,20,25,26,34,44,48], while others were based on the graph-cut method [4,7,12,16,17,27,31,42,54].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

Suárez-Mejías

Pérez-Carrasco

Serrano

et al. 2016

Med Biol Eng Comput

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An innovative algorithm has been developed for the segmentation of retroperitoneal tumors in 3D radiological images. This algorithm makes it possible for radiation oncologists and surgeons semiautomatically to select tumors for possible future radiation treatment and surgery. It is based on continuous convex relaxation methodology, the main novelty being the introduction of accumulated gradient distance, with intensity and gradient information being incorporated into the segmentation process. The algorithm was used to segment 26 CT image volumes. The results were compared with manual contouring of the same tumors. The proposed algorithm achieved 90 % sensitivity, 100 % specificity and 84 % positive predictive value, obtaining a mean distance to the closest point of 3.20 pixels. The algorithm's dependence on the initial manual contour was also analyzed, with results showing that the algorithm substantially reduced the variability of the manual segmentation carried out by different specialists. The algorithm was also compared with four benchmark algorithms (thresholding, edge-based level-set, region-based level-set and continuous max-flow with two labels). To the best of our knowledge, this is the first time the segmentation of retroperitoneal tumors for radiotherapy planning has been addressed.

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Section: Introductionmentioning

confidence: 99%

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

Suárez-Mejías

Pérez-Carrasco

Serrano

et al. 2016

Med Biol Eng Comput

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“…Ulas et al [30] demonstrated the effectiveness of segmenting lung tumor on PET images using random walk algorithm. Cherry et al [31] showed how to extract heart, liver and regions effectively which have similar uptake value to lesions by merging a novel monotonic downhill function with the conventional graph cut energy regularization.…”

Section: Related Workmentioning

confidence: 99%

“…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.

136

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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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“…Price et al [19] proposed a method for combining geodesic distance information with edge information in a graph cuts framework to complete image segmentation. Ballangan et al [20] used graph cuts in PET images for lung tumor segmentation, and extended the graph cuts with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. They can get the accurate contour of lung tumors, but the time consumption is very high.…”

Section: Introductionmentioning

confidence: 99%