Architectures and Applications of U-net in Medical Image Segmentation: A Review

Wang, Jundi; Han, Lei; Ran, Dongsheng

doi:10.1109/isssr58837.2023.00022

Cited by 3 publications

(1 citation statement)

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“…In the continuous quest to enhance the U-Net architecture for more nuanced and robust medical image processing, a substantial body of research has concentrated on modifications at the component level [20], [21]. These adjustments range from data augmentation strategies that enrich the input data spectrum, attention mechanisms that guide the model focus, and alterations in convolutional layers for more refined feature extraction to adaptations in pooling layers, skip connections, and comprehensive integrations of various modifications.…”

Section: Modifications To U-net Componentsmentioning

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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Section: Modifications To U-net Componentsmentioning

confidence: 99%