Convolutional Neural Networks Based Level Set Framework for Pancreas Segmentation from CT Images

Gong, Zhaoxuan; Zhu, Zhenyu; Zhang, Guodong; Zhao, Dazhe; Guo, Wei

doi:10.1145/3364836.3364842

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…International Symposium on Image Computing and Digital Medicine (ISICDM) 2018 Pancreatic Segmentation Challenge: This challenge zeroes in on the accurate diagnosis of pancreatic cancer, proffering exhaustively annotated data crafted manually. The employment of anonymous test data bolsters the transparency and reproducibility of investigative methodologies, spurring the genesis of avant-garde technologies and methodologies [66].…”

Section: Discussion and Analysis A Datasetsmentioning

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: Discussion and Analysis A Datasetsmentioning

confidence: 99%

“…As for algorithmic integration, Gong et al propose an optimised scheme that combines the level set method [66]. This scheme enables pixel-level probability maps generated by U-Net to serve as initial conditions for the level set method, achieving finer image segmentation.…”

Section: Integration With Other Modelsmentioning

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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“…Fractional differential enhancement highlights the fine details of the object, which can improve the contrast between liver and the surrounding tissues. 31 Fig. 2 exhibits the result of fractional differential enhancement.…”

Section: Fractional Differential Enhancementmentioning

confidence: 99%

A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images

Gong

Guo

et al. 2021

J Applied Clin Med Phys

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Accurate liver segmentation is essential for radiation therapy planning of hepatocellular carcinoma and absorbed dose calculation. However, liver segmentation is a challenging task due to the anatomical variability in both shape and size and the low contrast between liver and its surrounding organs. Thus we propose a convolutional neural network (CNN) for automated liver segmentation. In our method, fractional differential enhancement is firstly applied for preprocessing. Subsequently, an initial liver segmentation is obtained by using a CNN. Finally, accurate liver segmentation is achieved by the evolution of an active contour model. Experimental results show that the proposed method outperforms existing methods. One hundred fifty CT scans are evaluated for the experiment. For liver segmentation, Dice of 95.8%, true positive rate of 95.1%, positive predictive value of 93.2%, and volume difference of 7% are calculated. In addition, the values of these evaluation measures show that the proposed method is able to provide a precise and robust segmentation estimate, which can also assist the manual liver segmentation task.

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A Survey on Shape-Constraint Deep Learning for Medical Image Segmentation

Bohlender

Öksüz

Mukhopadhyay

2023

IEEE Rev. Biomed. Eng.

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Convolutional Neural Networks Based Level Set Framework for Pancreas Segmentation from CT Images

Cited by 3 publications

References 16 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

A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images

A Survey on Shape-Constraint Deep Learning for Medical Image Segmentation

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