Liver tumor detection in CT images by adaptive contrast enhancement and the EM/MPM algorithm

Masuda,; Tateyama,; Xiong, Jie; Zhou, Quan; Wakamiya,; Kanasaki,; Furukawa,; Chen, Guihai

doi:10.1109/icip.2011.6115708

Cited by 22 publications

(2 citation statements)

References 8 publications

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“…To demonstrate the effectiveness of the proposed method, it was compared with adaptively regularized kernel-based FCM (ARKFCM) [44], DRLSE [45], MAXENTROY [46], EM/MPM [47], continuous max-flow (CMF) [48], and OTSU [12]. MAXENTROPY, CMF, and OTSU methods are parameterless.…”

Section: Segmentation Resultsmentioning

confidence: 99%

An Enhanced U-Net Approach for Segmentation of Aeroengine Hollow Turbine Blade

et al. 2022

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The hollow turbine blade plays an important role in the propulsion of the aeroengine. However, due to its complex hollow structure and nickel-based superalloys material property, only industrial computed tomography (ICT) could realize its nondestructive detection with sufficient intuitiveness. The ICT detection precision mainly depends on the segmentation accuracy of target ICT images. However, because the hollow turbine blade is made of special superalloys and contains many small unique structures such as film cooling holes, exhaust edges, etc., the ICT image quality of the hollow turbine blades is often deficient, with artifacts, low contrast, and inhomogeneity scattered around the blade contour, making it hard for traditional mathematical model-based methods to acquire satisfying segmentation precision. Therefore, this paper presents a deep learning-based approach, i.e., the enhanced U-net with multiscale inputs, dense blocks, focal loss function, and residual path in the skip connection to realize the high-precision segmentation of the hollow turbine blade. The experimental results show that our proposed enhanced U-net can achieve better segmentation accuracy for practical turbine blades than conventional U-net and traditional mathematical model-based methods.

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Section: Segmentation Resultsmentioning

confidence: 99%

An Enhanced U-Net Approach for Segmentation of Aeroengine Hollow Turbine Blade

et al. 2022

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“…Billelo et al [10] utilized intensity-based histogram and liver contour refinement to segment liver tumors, followed by an SVM to sift out false alarms. Masuda et al [11] described a method to segment liver tumors by enhancing the CT scan contrast levels and expectation maximization of the posterior marginal. This super-voxel method, called EM/MPM, produced candidates that were then filtered using shape and location information.…”

Section: Introductionmentioning

confidence: 99%

Liver Tumor Segmentation Based on Multi-Scale Candidate Generation and Fractal Residual Network

Bai

Jiang

et al. 2019

IEEE Access

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Liver cancer is one of the most common cancers. Liver tumor segmentation is one of the most important steps in treating liver cancer. Accurate tumor segmentation on computed tomography (CT) images is a challenging task due to the variation of the tumor's shape, size, and location. To this end, this paper proposes a liver tumor segmentation method on CT volumes using multi-scale candidate generation method (MCG), 3D fractal residual network (3D FRN), and active contour model (ACM) in a coarse-to-fine manner. First, livers are segmented using 3D U-Net and then MCG is performed on these liver regions for obtaining tumor candidates (all superpixel blocks). Second, 3D FRN is proposed to further determine tumor regions, which is considered as coarse segmentation results. Finally, the ACM is used for tumor segmentation refinement. The proposed 3D MCG-FRN + ACM is trained using the 110 cases in the LiTS dataset and evaluated on a public liver tumor dataset of the 3DIRCADb with dice per case of 0.67. The experimentations and comparisons demonstrate the performance advantage of the 3D MCG-FRN + ACM compared to other segmentation methods. INDEX TERMSFractal residual network, multi-scale candidate generation method, active contour model, liver tumor segmentation, CT volume. ZHIQI BAI received the B.S. degree from the Department of Software Engineering, Northeastern University, China, in 2017, where he is currently a bachelor's degree with the Department of Software Engineering School. His research interests include medical image analysis and machine learning.

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A Proposal for Color Segmentation in PET/CT-Guided Liver Images

Bangar

Sharma

2014

ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol II

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Liver tumor detection in CT images by adaptive contrast enhancement and the EM/MPM algorithm

Cited by 22 publications

References 8 publications

An Enhanced U-Net Approach for Segmentation of Aeroengine Hollow Turbine Blade

An Enhanced U-Net Approach for Segmentation of Aeroengine Hollow Turbine Blade

Liver Tumor Segmentation Based on Multi-Scale Candidate Generation and Fractal Residual Network

A Proposal for Color Segmentation in PET/CT-Guided Liver Images

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