Accurate pancreas segmentation using multi-level pyramidal pooling residual U-Net with adversarial mechanism

Li, Meiyu; Lian, Fenghui; Wang, Chunyu; Guo, Shuxu

doi:10.1186/s12880-021-00694-1

Cited by 18 publications

(7 citation statements)

References 18 publications

(9 reference statements)

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“…Adversarial loss combined with multi-level pyramid pooling modules [39] optimises the multi-scale representation of features and strengthens the model's discriminative ability for different structures and textures. The merit lies in its excellent performance in handling class imbalance and irregular target segmentation, which may lead to model overfitting [39].…”

Section: Convolution Block Modificationsmentioning

confidence: 99%

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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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Section: Convolution Block Modificationsmentioning

confidence: 99%

“…Multi-Level Pyramid Pooling Residual U-Net improves performance by incorporating a Multi-Level Pyramid Pooling (MLPP) module [39]. MLPP employs pooling at various scales to capture multi-scale information, combined with residual learning and adversarial training.…”

Section: E Alternative Pooling Strategymentioning

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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“…Li et al (Li et al, 2021) aimed to segmentation of the pancreas using abdominal CT images. The NIH data set was used in this study.…”

Section: Related Workmentioning

confidence: 99%

CT Modalitesinde U-Net Tabanlı Segmentasyon Ağlarını Kullanarak Pankreas Segmentasyonu: Karşılaştırmalı Bir Analiz

DERİN

GURKAN

BUDAK³

et al. 2022

EJOSAT

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The pancreas is one of the small size organs in the abdomen. Moreover, anatomical differences make it difficult to detect the pancreas. This project aims to automatically segmentation of pancreas. For this purpose, NIH-CT82 data set, which includes CT images from 82 patients was used. U-Net which is state-of-the-art model and its different versions, namely Attention U-Net, Residual U-Net, Attention Residual U-Net, and Residual U-Net++ were tested. Best predict performance was achieved by Residual U-Net with the dice of 0.903, IoU of 0.823, sensitivity of 0.898, specificity of 1.000, precision of 0.908, and accuracy of 0.999. Consequently, an artificial intelligence (AI) supported decision support system was created for pancreas segmentation.

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“…Therefore, such a deficiency in capturing multi-scale information yields a performance degradation in the segmentation of complex structures, with variation in shapes and scales. Different methods have been proposed to solve the problem of the restricted receptive field of regular CNNs in recent years [11,22]. Wang et al [29] extended the self-attention concept into the spatial domain to model non-local properties of images by devising a non-local module that can be easily integrated into existing network designs.…”

Section: Introductionmentioning

confidence: 99%

Contextual Attention Network: Transformer Meets U-Net

Azad¹,

Heidari²,

Wu³

et al. 2022

Preprint

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Currently, convolutional neural networks (CNN) (e.g., U-Net) have become the de facto standard and attained immense success in medical image segmentation. However, as a downside, CNN based methods are a double-edged sword as they fail to build long-range dependencies and global context connections due to the limited receptive field that stems from the intrinsic characteristics of the convolution operation. Hence, recent articles have exploited Transformer variants for medical image segmentation tasks which open up great opportunities due to their innate capability of capturing long-range correlations through the attention mechanism. Although being feasibly designed, most of the cohort studies incur prohibitive performance in capturing local information, thereby resulting in less lucidness of boundary areas. In this paper, we propose a contextual attention network to tackle the aforementioned limitations. The proposed method uses the strength of the Transformer module to model the long-range contextual dependency. Simultaneously, it utilizes the CNN encoder to capture local semantic information. In addition, an object-level representation is included to model the regional interaction map. The extracted hierarchical features are then fed to the contextual attention module to adaptively recalibrate the representation space using the local information. Then, they emphasize the informative regions while taking into account the long-range contextual dependency derived by the Transformer module. We validate our method on several large-scale public medical image segmentation datasets and achieve state-of-the-art performance. We have provided the implementation code in github.

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Accurate pancreas segmentation using multi-level pyramidal pooling residual U-Net with adversarial mechanism

Cited by 18 publications

References 18 publications

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

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

CT Modalitesinde U-Net Tabanlı Segmentasyon Ağlarını Kullanarak Pankreas Segmentasyonu: Karşılaştırmalı Bir Analiz

Contextual Attention Network: Transformer Meets U-Net

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