Recent Advances of Endoscopic Ultrasound in Diagnostics and Therapeutics

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1071

Special Issue Editor


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Special Issue Information

Dear Colleagues,

This Special Issue of "Recent Advances of Endoscopic Ultrasound in Diagnostics" focuses on the remarkable progress made in the utilization of endoscopic ultrasound (EUS) for diagnostic applications. This edition highlights the innovative techniques and advancements in EUS imaging that have significantly improved the detection, characterization, and staging of various gastrointestinal and pancreaticobiliary diseases. Key advancements include the development of high-resolution probes, enhanced imaging modalities such as elastography and contrast-enhanced EUS, and the integration of EUS with other diagnostic modalities like confocal laser endomicroscopy. These advancements have enabled more precise diagnosis of malignancies, inflammatory processes, and other pathological conditions. This Special Issue also explores the challenges and limitations in EUS diagnostics, as well as future directions for continued improvement in this evolving field.

Dr. Antonio Facciorusso
Guest Editor

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Keywords

  • endoscopic ultrasound (EUS)
  • diagnostic advances
  • image resolution
  • tissue sampling
  • echoendoscopy

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Published Papers (1 paper)

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Research

12 pages, 2159 KiB  
Article
Adaptive Evolutionary Optimization of Deep Learning Architectures for Focused Liver Ultrasound Image Segmentation
by Ali Zifan, Katelyn Zhao, Madilyn Lee, Zihan Peng, Laura J. Roney, Sarayu Pai, Jake T. Weeks, Michael S. Middleton, Ahmed El Kaffas, Jeffrey B. Schwimmer and Claude B. Sirlin
Diagnostics 2025, 15(2), 117; https://doi.org/10.3390/diagnostics15020117 - 7 Jan 2025
Viewed by 675
Abstract
Background: Liver ultrasound segmentation is challenging due to low image quality and variability. While deep learning (DL) models have been widely applied for medical segmentation, generic pre-configured models may not meet the specific requirements for targeted areas in liver ultrasound. Quantitative ultrasound (QUS) [...] Read more.
Background: Liver ultrasound segmentation is challenging due to low image quality and variability. While deep learning (DL) models have been widely applied for medical segmentation, generic pre-configured models may not meet the specific requirements for targeted areas in liver ultrasound. Quantitative ultrasound (QUS) is emerging as a promising tool for liver fat measurement; however, accurately segmenting regions of interest within liver ultrasound images remains a challenge. Methods: We introduce a generalizable framework using an adaptive evolutionary genetic algorithm to optimize deep learning models, specifically U-Net, for focused liver segmentation. The algorithm simultaneously adjusts the depth (number of layers) and width (neurons per layer) of the network, dropout, and skip connections. Various architecture configurations are evaluated based on segmentation performance to find the optimal model for liver ultrasound images. Results: The model with a depth of 4 and filter sizes of [16, 64, 128, 256] achieved the highest mean adjusted Dice score of 0.921, outperforming the other configurations, using three-fold cross-validation with early stoppage. Conclusions: Adaptive evolutionary optimization enhances the deep learning architecture for liver ultrasound segmentation. Future work may extend this optimization to other imaging modalities and deep learning architectures. Full article
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