Automatic Pancreas Segmentation Using Double Adversarial Networks With Pyramidal Pooling Module

Li, Meiyu; Lian, Fenghui; Guo, Shuxu

doi:10.1109/access.2021.3118718

Cited by 12 publications

(3 citation statements)

References 40 publications

(67 reference statements)

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“…Auxiliary translation enhances low-level information and extracts the invariant modalities' properties, which ultimately enhances multimodal segmentation's performance. In paper [14], a double adversarial U-Net architecture is created by fusing the adversarial learning capabilities of GAN with the U-Net structure. Instead of the original U-Net pooling layers, which are capable of maintaining contextual information, a pyramidal pooling module is added to the dual adversarial networks in order to improve the pancreatic segmentation performance.…”

Section: Methodsmentioning

confidence: 99%

An overview of abdominal segmentation methods using General Adversarial Network (GAN)

LIU

2022

International Conference on Biomedical and Intelligent Systems (IC-BIS 2022)

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Applications ranging from image-guided surgery to computer-assisted diagnosis depend on the segmentation of abdominal anatomy. The uses of the General Adversarial Network (GAN) in the automatic segmentation of abdominal pictures are discussed in this review article. Discussion in this paper starts off by reviewing the underlying theory of GAN and its variations, then covering the most recent abdominal segmentation techniques. In addition, it introduces the variety of applications and datasets included for these imaging studies. The quantitative performances (evaluated using Dice Similarity Coefficient and F-scores) of 13 representative studies are summarized and compared. Overall, a more precise abdominal segmentation can be achieved using GAN-based methods of image analysis.

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Section: Methodsmentioning

confidence: 99%

An overview of abdominal segmentation methods using General Adversarial Network (GAN)

LIU

2022

International Conference on Biomedical and Intelligent Systems (IC-BIS 2022)

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“…Dual Adversarial U-Net introduces innovative design elements [49]. In addition to replacing standard pooling, the model integrates attention mechanisms to optimise target recognition.…”

Section: E Alternative Pooling Strategymentioning

confidence: 99%

“…Li et al also introduce an innovative Dual Adversarial U-Net incorporating Generative Adversarial Networks (GANs) and pyramid pooling modules [49]. These combined technological elements improve the synergistic performance between the segment and the discriminator, allowing the model to capture key information at different scales.…”

Section: B Parallel U-netsmentioning

confidence: 99%

State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its Variants

Zhang,

Achuthan,

Himel

2024

IEEE Access

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In medical image analysis, segmenting pancreatic CT images presents a significant challenge due to the complex anatomy of the pancreas and the generally low contrast of these images. Accurate pancreas segmentation is crucial in clinical scenarios, particularly for the diagnosis and treatment of pancreatic cancer. The U-Net architecture and its variations have achieved significant progress in deep learningbased image segmentation, especially in the context of pancreatic CT image segmentation. However, there is a noticeable gap in the comprehensive evaluation of their performance, limitations, and potential improvements specifically in this area. This systematic review aims to address this gap in the literature, focusing particularly on U-Net and its variants in pancreatic CT image segmentation. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes relevant studies published since 2019 in the field of pancreatic segmentation. The findings illuminate the current limitations of these methods and establish a theoretical foundation for future research directions.

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CTUNet: automatic pancreas segmentation using a channel-wise transformer and 3D U-Net

Chen

Wan

2022

Vis Comput

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Automatic Pancreas Segmentation Using Double Adversarial Networks With Pyramidal Pooling Module

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 12 publications

References 40 publications

An overview of abdominal segmentation methods using General Adversarial Network (GAN)

An overview of abdominal segmentation methods using General Adversarial Network (GAN)

State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its Variants

CTUNet: automatic pancreas segmentation using a channel-wise transformer and 3D U-Net

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