Application of deep-learning to the automatic segmentation and classification of lateral lymph nodes on ultrasound images of papillary thyroid carcinoma

Yuan, Yuquan; Hou, Shaodong; Wu, Xing; Wang, Yuteng; Sun, Yiceng; Yang, Zeyu; Yin, Supeng; Zhang, Fan

doi:10.1016/j.asjsur.2024.02.140

Cited by 1 publication

(1 citation statement)

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“…In the essential phase of image processing and analysis, this study employed an advanced deep-learning architecture, specifically the Mask2Former model, to analyze ultrasound (US) images captured during radiofrequency ablation (RFA) procedures. Mask2Former was chosen because of its high accuracy for ultrasound imaging processing compared to other architectures like Mask R-CNN or SOLO [30]. The initial step involved adopting the foundational code of the Mask2Former model to handle the segmentation of the AZ within US images.…”

Section: Image Processing Deep-learning Model and Analysismentioning

confidence: 99%

Application of Deep Learning for Real-Time Ablation Zone Measurement in Ultrasound Imaging

Zimmermann,

Michelmann,

Daniel

et al. 2024

Cancers

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Background: The accurate delineation of ablation zones (AZs) is crucial for assessing radiofrequency ablation (RFA) therapy’s efficacy. Manual measurement, the current standard, is subject to variability and potential inaccuracies. Aim: This study aims to assess the effectiveness of Artificial Intelligence (AI) in automating AZ measurements in ultrasound images and compare its accuracy with manual measurements in ultrasound images. Methods: An in vitro study was conducted using chicken breast and liver samples subjected to bipolar RFA. Ultrasound images were captured every 15 s, with the AI model Mask2Former trained for AZ segmentation. The measurements were compared across all methods, focusing on short-axis (SA) metrics. Results: We performed 308 RFA procedures, generating 7275 ultrasound images across liver and chicken breast tissues. Manual and AI measurement comparisons for ablation zone diameters revealed no significant differences, with correlation coefficients exceeding 0.96 in both tissues (p < 0.001). Bland–Altman plots and a Deming regression analysis demonstrated a very close alignment between AI predictions and manual measurements, with the average difference between the two methods being −0.259 and −0.243 mm, for bovine liver and chicken breast tissue, respectively. Conclusion: The study validates the Mask2Former model as a promising tool for automating AZ measurement in RFA research, offering a significant step towards reducing manual measurement variability.

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Section: Image Processing Deep-learning Model and Analysismentioning

confidence: 99%