Application of U-Net and Optimized Clustering in Medical Image Segmentation: A Review

Shao, Jinchen; Chen, Shuwen; Zhou, Jin; Zhu, Huisheng; Wang, Ziyi; Brown, Mackenzie

doi:10.32604/cmes.2023.025499

Cited by 10 publications

(8 citation statements)

References 144 publications

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“…The recognition results of the test set are shown in Table 3. In addition, binary descriptor-based SURF [25][26][27] and U-net technique [28,29] are being widely deployed for recognition systems. For comparison, the results identified by these two methods are also included in Table 3.…”

Section: Resultsmentioning

confidence: 99%

Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

Wang,

Hu,

Yin

et al. 2024

Applied Sciences

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To address the recognition challenges faced by arrester pointer instruments’ dial scales in various scenarios, this paper introduces a deep learning-based recognition method for pointer instrument scales. An attention module is integrated into the YOLOv5 network architecture, enhancing the accuracy and robustness of the model. After correcting the dial, dial recognition is conducted with OpenCV to achieve precise identification of the instrument scales. The proposed method was tested using images of arrester pointer instruments against diverse backgrounds. The experimental results demonstrate that the method processes instrument data images in an average time of 0.662 s and achieves a successful recognition rate of 96% with an average error of 0.923%. This method provides a rapid and efficient approach for recognizing instrument scales and offers a novel solution for identifying similar types of instruments.

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Section: Resultsmentioning

confidence: 99%

Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

Wang,

Hu,

Yin

et al. 2024

Applied Sciences

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“…The authors claim 3D U-Net, Adversial U-Net, and Attention U-Bet as the main variants. In the research [15], the authors point out 2.5D U-Net, 3D U-Net, Context Nested U-Net, all connection U-Net, RU-Net, and VGG16 U-Net as the main variants. They review performances and features in the context of segmentation disadvantages.…”

Section: Related Workmentioning

confidence: 99%

An overview of image processing in biomedicine using U-Net convolutional neural network architecture

Komosar,

Stefanović,

Sladojević

2024

J Comp Foren Sci

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Image processing in biomedicine is a very broad field, which includes both medical and technical significance. The aim of this paper is to investigate the current trends in the domain of application of U-Net architecture in the period from 2018 to 2023. The PRISMA framework was used for the systematic literature review, and 4 research questions were asked. For the most part, U-Net architectures are used that can process complex high-resolution images in the fastest way in the context of semantic segmentation. Previous work in image processing has focused on overcoming problems such as the complexity of different architectures, image loss, image resolution, and quality, as well as the size of datasets and noise reduction. The most frequently used groups of datasets are BraTS, Data Science Bowl, and ISIC Challenge. The best general Dice score was obtained for the LUNA16, VESSEL12, and Kaggle Lung datasets with 0.98. It is concluded that the application of the U-Net network is growing, with a focus on solving specific challenges in the context of a certain modality and segment of biomedicine.

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“…Many reviews have been published on automatic segmentation methods for specific regions of interest [17][18][19][20][21][22][23][24][25][26], image modalities [17,20,24,27,28], and methods [29][30][31][32][33][34][35][36][37][38][39][40]. How-ever, none of these thoroughly cover all three aspects to provide a holistic overview of the state of the field.…”

Section: Related Workmentioning

confidence: 99%

Automatic Segmentation with Deep Learning in Radiotherapy

Isaksson,

Summers,

Mastroleo

et al. 2023

Cancers

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This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: “What should researchers think about when starting a segmentation study?”, “How can research practices in medical image segmentation be improved?”, “What is missing from the current corpus?”, and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today’s competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information.

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Application of U-Net and Optimized Clustering in Medical Image Segmentation: A Review

Cited by 10 publications

References 144 publications

Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

An overview of image processing in biomedicine using U-Net convolutional neural network architecture

Automatic Segmentation with Deep Learning in Radiotherapy

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