Accounting for Domain Shift in Neural Network Ultrasound Beamforming

Tierney, Jaime; Luchies, Adam; Khan, Christopher; Byram, Brett; Berger, Matthew

doi:10.1109/ius46767.2020.9251577

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…While the results were promising in both phantoms and ex vivo vessels, it is possible that performance could be improved using a domain adaptation technique. While acquiring large clinical datasets for training can be impractical, and the ground truth is usually based on imperfect and costly annotations, the proposed approach may benefit from training on experimental data sets or additional processing steps that improve performance by accounting for the domain shift 93–96 …”

Section: Discussionmentioning

confidence: 99%

“…While acquiring large clinical datasets for training can be impractical, and the ground truth is usually based on imperfect and costly annotations, the proposed approach may benefit from training on experimental data sets or additional processing steps that improve performance by accounting for the domain shift. [93][94][95][96] Finally, further studies are required to assess the ability of the developed forward-viewing system with robotic steering to be used for remote procedures. With integrated imaging and robotic actuation, the proposed guidewire could be remotely controlled, giving enhanced revascularization ability in any clinic that performs vascular access procedures.…”

Section: Future Considerationsmentioning

confidence: 99%

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Supervised segmentation for guiding peripheral revascularization with forward‐viewing, robotically steered ultrasound guidewire

et al. 2023

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Background Approximately 500 000 patients present with critical limb ischemia (CLI) each year in the U.S., requiring revascularization to avoid amputation. While peripheral arteries can be revascularized via minimally invasive procedures, 25% of cases with chronic total occlusions are unsuccessful due to inability to route the guidewire beyond the proximal occlusion. Improvements to guidewire navigation would lead to limb salvage in a greater number of patients. Purpose Integrating ultrasound imaging into the guidewire could enable direct visualization of routes for guidewire advancement. In order to navigate a robotically‐steerable guidewire with integrated imaging beyond a chronic occlusion proximal to the symptomatic lesion for revascularization, acquired ultrasound images must be segmented to visualize the path for guidewire advancement. Methods The first approach for automated segmentation of viable paths through occlusions in peripheral arteries is demonstrated in simulations and experimentally‐acquired data with a forward‐viewing, robotically‐steered guidewire imaging system. B‐mode ultrasound images formed via synthetic aperture focusing (SAF) were segmented using a supervised approach (U‐net architecture). A total of 2500 simulated images were used to train the classifier to distinguish the vessel wall and occlusion from viable paths for guidewire advancement. First, the size of the synthetic aperture resulting in the highest classification performance was determined in simulations (90 test images) and compared with traditional classifiers (global thresholding, local adaptive thresholding, and hierarchical classification). Next, classification performance as a function of the diameter of the remaining lumen (0.5 to 1.5 mm) in the partially‐occluded artery was tested using both simulated (60 test images at each of 7 diameters) and experimental data sets. Experimental test data sets were acquired in four 3D‐printed phantoms from human anatomy and six ex vivo porcine arteries. Accuracy of classifying the path through the artery was evaluated using microcomputed tomography of phantoms and ex vivo arteries as a ground truth for comparison. Results An aperture size of 3.8 mm resulted in the best‐performing classification based on sensitivity and Jaccard index, with a significant increase in Jaccard index (p < 0.05) as aperture diameter increased. In comparing the performance of the supervised classifier and traditional classification strategies with simulated test data, sensitivity and F1 score for U‐net were 0.95 ± 0.02 and 0.96 ± 0.01, respectively, compared to 0.83 ± 0.03 and 0.41 ± 0.13 for the best‐performing conventional approach, hierarchical classification. In simulated test images, sensitivity (p < 0.05) and Jaccard index both increased with increasing artery diameter (p < 0.05). Classification of images acquired in artery phantoms with remaining lumen diameters ≥ 0.75 mm resulted in accuracies > 90%, while mean accuracy decreased to 82% when artery diameter decreased to 0.5 mm. For testing i...

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Section: Discussionmentioning

confidence: 99%

Section: Future Considerationsmentioning

confidence: 99%

Supervised segmentation for guiding peripheral revascularization with forward‐viewing, robotically steered ultrasound guidewire

et al. 2023

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International multicenter validation of AI-driven ultrasound detection of ovarian cancer

Christiansen,

Konuk,

Ganeshan

et al. 2025

Nat Med

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Ovarian lesions are common and often incidentally detected. A critical shortage of expert ultrasound examiners has raised concerns of unnecessary interventions and delayed cancer diagnoses. Deep learning has shown promising results in the detection of ovarian cancer in ultrasound images; however, external validation is lacking. In this international multicenter retrospective study, we developed and validated transformer-based neural network models using a comprehensive dataset of 17,119 ultrasound images from 3,652 patients across 20 centers in eight countries. Using a leave-one-center-out cross-validation scheme, for each center in turn, we trained a model using data from the remaining centers. The models demonstrated robust performance across centers, ultrasound systems, histological diagnoses and patient age groups, significantly outperforming both expert and non-expert examiners on all evaluated metrics, namely F1 score, sensitivity, specificity, accuracy, Cohen’s kappa, Matthew’s correlation coefficient, diagnostic odds ratio and Youden’s J statistic. Furthermore, in a retrospective triage simulation, artificial intelligence (AI)-driven diagnostic support reduced referrals to experts by 63% while significantly surpassing the diagnostic performance of the current practice. These results show that transformer-based models exhibit strong generalization and above human expert-level diagnostic accuracy, with the potential to alleviate the shortage of expert ultrasound examiners and improve patient outcomes.

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