Dynamic Perfusion Representation and Aggregation Network for Nodule Segmentation Using Contrast-Enhanced US

Wan, Peng; Haiyan, Xue; Liu, Chunrui; Chen, Fang; Kong, Wentao; Zhang, Daoqiang

doi:10.1109/jbhi.2023.3270307

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…The primary challenge of dynamic CEUS-based segmentation is modelling the dynamic enhancement evolution patterns associated with differences in blood supply status. To effectively represent real-time enhancement characteristics and aggregate them in a global view, Wan et al introduced the perfusion excitation gate and cross-attention temporal aggregation module for the automatic segmentation of lesions using dynamic CEUS imaging ( 70 ). Other authors proposed a trans-CEUS model for CEUS analysis for spatial temporal separation aggregation and global spatial-temporal fusion methods, with the aim of extracting the enhanced perfusion from the dynamic CEUS picture ( 71 ).…”

Section: Overview Of DLmentioning

confidence: 99%

A narrative review of deep learning in thyroid imaging: current progress and future prospects

Yang,

Ma,

Chen

2024

Quant Imaging Med Surg

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Background and Objective Deep learning (DL) has contributed substantially to the evolution of image analysis by unlocking increased data and computational power. These DL algorithms have further facilitated the growing trend of implementing precision medicine, particularly in areas of diagnosis and therapy. Thyroid imaging, as a routine means to screening for thyroid diseases on large-scale populations, is a massive data source for the development of DL models. Thyroid disease is a global health problem and involves structural and functional changes. The objective of this study was to evaluate the general rules and future directions of DL networks in thyroid medical image analysis through a review of original articles published between 2018 and 2023. Methods We searched for English-language articles published between April 2018 and September 2023 in the databases of PubMed, Web of Science, and Google Scholar. The keywords used in the search included artificial intelligence or DL, thyroid diseases, and thyroid nodule or thyroid carcinoma. Key Content and Findings The computer vision tasks of DL in thyroid imaging included classification, segmentation, and detection. The current applications of DL in clinical workflow were found to mainly include management of thyroid nodules/carcinoma, risk evaluation of thyroid cancer metastasis, and discrimination of functional thyroid diseases. Conclusions DL is expected to enhance the quality of thyroid images and provide greater precision in the assessment of thyroid images. Specifically, DL can increase the diagnostic accuracy of thyroid diseases and better inform clinical decision-making.

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Section: Overview Of DLmentioning

confidence: 99%