Claw U-Net: A Unet-based Network with Deep Feature Concatenation for Scleral Blood Vessel Segmentation

Yao, Chen; Tang, Jingyu; Hu, Menghan; Wu, Yue; Guo, Wenyi; Li, Qingli; Xiao-ping, Zhang

doi:10.48550/arxiv.2010.10163

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…For example, in Germany, a patient must give informed consent to the use of AI in the process of his diagnosis and treatment, which we believe is a good practice. Also, rules that should be fulfilled by the AI-based system, like the Assessment List for Trustworthy Artificial Intelligence (ALTAI) [207][208][209][210], have been formulated. In [211,212], 10 ethical risk points (ERPs) important to institutions, policymakers, teachers, students, and patients, including potential impacts on design, content, delivery, and AI-human communication in the field of AI and metaverse-based medical education, were defined.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Rudnicka,

Szczepanski,

Pregowska

2024

Electronics

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Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Rudnicka,

Szczepanski,

Pregowska

2024

Electronics

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“…Few works have also investigated the effectiveness of Transformer layers by integrating them into the encoder of UNet-based architectures in a plug-and-play manner. For instance, Cheng et al [165] propose TransClaw UNet by integrating Transformer layers in the encoding part of the Claw UNet [166] to exploit multi-scale information. TransClaw-UNet achieves an absolute gain of 0.6 in dice score compared to Claw-UNet on Synapse multi-organ segmentation dataset and shows excellent generalization.…”

Section: Hybrid Architecturesmentioning

confidence: 99%

Transformers in Medical Imaging: A Survey

Shamshad¹,

Khan²,

Zamir³

et al. 2022

Preprint

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Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, reconstruction, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.

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“…( 1) The RT is used to learn texture information. It extracts texture information by an architecture based on Swin-Unet [15] and Claw U-Net [9], which treats Swin Transformer Blocks as encoders, decoders, and bottlenecks. Also, it includes skip connections to get global information of input images for better segmentation performance.…”

Section: A Architecture Overviewmentioning

confidence: 99%

“…The function of skip connection is to fuse the low-level features, which can compensate for the location information and alleviate the loss caused by down-sampling operations. After the appearance of U-Net, many scholars have also proposed lots of methods based on U-Net, which have achieved better segmentation performance, such as SAUNet [4], LinkNet [5], UNet++ [6], UNet 3+ [7], Attention-UNet [8], Claw U-Net [9] and so on. The performance of these methods has demonstrated that CNNs have a notable ability to learn discriminating features, especially textural features.…”

Section: Introductionmentioning

confidence: 99%

MCRformer: Morphological Constraint Reticular Transformer for 3D Medical Image Segmentation

Li¹,

Chen²,

Han³

et al. 2023

Preprint

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<p>Medical image segmentation is essential in medical image analysis since it can provide reliable assistance in computer-aided clinical diagnosis, treatment plan-ning, and intervention. Although deep learning algorithms based on CNNs and Transformers have made notable progress in conventional image segmentation, medical image segmentation is still challenging owing to limited ability to learn the prior information about the target shapes, especially for large target areas, which have the problems of rough boundaries in the segmentation results, such as over-segmentation, under-segmentation and internal holes. To alleviate these issues, we propose a novel 3D medical image segmentation network based on Transformer Blocks and the CNN model by fusing morphological information, and design a Reticular Transformer (RT) by the reticular mechanism. Specifically, firstly, the morphological constraint stream is designed to learn the prior shape structure information based on the CNN model. Secondly, the Reticular Transformer is utilized to obtain multi-scale information based on the Transformer, which can further utilize the local texture information and underlying semantic information to obtain sufficient details and receptive field. The experiments on the datasets demonstrate that our proposed method outperforms many existing segmentation models in terms of the performance in metrics DSC and HD (80.46\% in DSC on the Synapse dataset and 90.83\% in DSC on the ACDC dataset). The code will be released at https://github.com/rocklijun/MCRformer.</p>

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Claw U-Net: A Unet-based Network with Deep Feature Concatenation for Scleral Blood Vessel Segmentation

Cited by 4 publications

References 17 publications

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Transformers in Medical Imaging: A Survey

MCRformer: Morphological Constraint Reticular Transformer for 3D Medical Image Segmentation

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