Automated Cosegmentation of Tumor Volume and Metabolic Activity Using PET-CT in Non-Small Cell Lung Cancer (NSCLC)

Li, H.; Bai, Junjie; Hejle, T. Abu; Wu, Xiaodong; Bhatia, S.K.; Kim, Y.

doi:10.1016/j.ijrobp.2013.06.1396

Cited by 4 publications

(10 citation statements)

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“…11,12 Substantial progress has been made in automating the tumor definition extracted from PET-CT scans. [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] Although these methods showed promise, there are still limitations when adapting them for clinical use. Most previous methods depend on user-defined foreground seeds belonging to the tumor.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

et al. 2019

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Purpose To investigate the use and efficiency of 3‐D deep learning, fully convolutional networks (DFCN) for simultaneous tumor cosegmentation on dual‐modality nonsmall cell lung cancer (NSCLC) and positron emission tomography (PET)‐computed tomography (CT) images. Methods We used DFCN cosegmentation for NSCLC tumors in PET‐CT images, considering both the CT and PET information. The proposed DFCN‐based cosegmentation method consists of two coupled three‐dimensional (3D)‐UNets with an encoder‐decoder architecture, which can communicate with the other in order to share complementary information between PET and CT. The weighted average sensitivity and positive predictive values denoted as Scores, dice similarity coefficients (DSCs), and the average symmetric surface distances were used to assess the performance of the proposed approach on 60 pairs of PET/CTs. A Simultaneous Truth and Performance Level Estimation Algorithm (STAPLE) of 3 expert physicians’ delineations were used as a reference. The proposed DFCN framework was compared to 3 graph‐based cosegmentation methods. Results Strong agreement was observed when using the STAPLE references for the proposed DFCN cosegmentation on the PET‐CT images. The average DSCs on CT and PET are 0.861 ± 0.037 and 0.828 ± 0.087, respectively, using DFCN, compared to 0.638 ± 0.165 and 0.643 ± 0.141, respectively, when using the graph‐based cosegmentation method. The proposed DFCN cosegmentation using both PET and CT also outperforms the deep learning method using either PET or CT alone. Conclusions The proposed DFCN cosegmentation is able to outperform existing graph‐based segmentation methods. The proposed DFCN cosegmentation shows promise for further integration with quantitative multimodality imaging tools in clinical trials.

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Section: Introductionmentioning

confidence: 99%

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

et al. 2019

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“…Recently, the co-segmentation technique for tumor delineation using both PET-CT has attracted great attentions [3, 4, 5, 6, 7], where tumor contours on PET and on CT are segmented simultaneously while admitting their possible differences to accommodate the registration inaccuracy and imaging uncertainty. It has demonstrated in those previous works that the design of cost functions in the framework of graph-cut based co-segmentation is critical to achieve good segmentation performance.…”

Section: Introductionmentioning

confidence: 99%

“…Representative work on cost function design include using sophisticated image priors (e.g., Gaussian mixture models [3, 4], texture information [6], shape prior [7], etc.) and/or clinical information [4, 5, 6, 7]. More recently, Zhong et al [8] have introduced the 3D alpha matting technique to compute the region costs for co-segmentation on PET-CT images and validate the efficiency.…”

Section: Introductionmentioning

confidence: 99%

3D fully convolutional networks for co-segmentation of tumors on PET-CT images

Zhi

Kim

Zhou

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

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Positron emission tomography and computed tomography (PET-CT) dual-modality imaging provides critical diagnostic information in modern cancer diagnosis and therapy. Automated accurate tumor delineation is essentially important in computer-assisted tumor reading and interpretation based on PET-CT. In this paper, we propose a novel approach for the segmentation of lung tumors that combines the powerful fully convolutional networks (FCN) based semantic segmentation framework (3D-UNet) and the graph cut based co-segmentation model. First, two separate deep UNets are trained on PET and CT, separately, to learn high level discriminative features to generate tumor/non-tumor masks and probability maps for PET and CT images. Then, the two probability maps on PET and CT are further simultaneously employed in a graph cut based co-segmentation model to produce the final tumor segmentation results. Comparative experiments on 32 PET-CT scans of lung cancer patients demonstrate the effectiveness of our method.

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“…Recently, the co-segmentation technique for tumor delineation on both PET and CT images has been attracted great attentions [3, 4, 5, 6, 7]. In those works, tumor contours on PET and on CT are segmented simultaneously while admitting their possible differences.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the region/unary costs were usually designed carefully based on some sophisticated image priors (e.g., Gaussian mixture models [3, 4], shape prior [7], texture information [6], …etc.) or clinical information from expertise [4, 5, 6, 7]. …”

Section: Introductionmentioning

confidence: 99%

Improving tumor co-segmentation on PET-CT images with 3D co-matting

Zhi

Kim

Zhou

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

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Positron emission tomography and computed tomography (PET-CT) plays a critically important role in modern cancer therapy. In this paper, we focus on automated tumor delineation on PET-CT image pairs. Inspired by co-segmentation model, we develop a novel 3D image co-matting technique making use of the inner-modality information of PET and CT for matting. The obtained co-matting results are then incorporated in the graph-cut based PET-CT co-segmentation framework. Our comparative experiments on 32 PET-CT scan pairs of lung cancer patients demonstrate that the proposed 3D image co-matting technique can significantly improve the quality of cost images for the co-segmentation, resulting in highly accurate tumor segmentation on both PET and CT scan pairs.

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Automated Cosegmentation of Tumor Volume and Metabolic Activity Using PET-CT in Non-Small Cell Lung Cancer (NSCLC)

Cited by 4 publications

References 0 publications

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

3D fully convolutional networks for co-segmentation of tumors on PET-CT images

Improving tumor co-segmentation on PET-CT images with 3D co-matting

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