Domain Adaptation for Ultrasound Beamforming

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

doi:10.1007/978-3-030-59713-9_40

Cited by 11 publications

(9 citation statements)

References 26 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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“…In the future, it is worth trying to use experimentally-acquired data for training though. Alternatively, a cycle generative adversarial network approach may be used [31] , which circumvents the experimental acquisition of high-resolution AR-PAM images. Briefly, simulated ground truth and simulated low-resolution image pairs (paired data) in conjunction with experimental low-resolution images (i.e., unpaired data) can be generated and experimentally acquired, and finally, experimental high-resolution images (corresponding to the experimental low-resolution images) could be generated to obtain paired data for training.…”

Section: Discussionmentioning

confidence: 99%

High-fidelity deconvolution for acoustic-resolution photoacoustic microscopy enabled by convolutional neural networks

et al. 2022

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“…The performance of these approaches depends on how well the approximation mitigates the distribution shift. Other approaches attempt to learn data augmentation by training a generative model between testing and training domains [16]- [19]. On the other hand, domain generalization approaches aim to recover feature representations that are independent of domains [20]- [22].…”

Section: Highlightsmentioning

confidence: 99%

Machine-to-Machine Transfer Function in Deep Learning-Based Quantitative Ultrasound

Soylu,

Oelze

2024

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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A Transfer Function approach was recently demonstrated to mitigate data mismatches at the acquisition level for a single ultrasound scanner in deep learning (DL) based quantitative ultrasound (QUS). As a natural progression, we further investigate the transfer function approach and introduce a Machine-to-Machine (M2M) Transfer Function, which possesses the ability to mitigate data mismatches at a machine level. This ability opens the door to unprecedented opportunities for reducing DL model development costs, enabling the combination of data from multiple sources or scanners, or facilitating the transfer of DL models between machines. We tested the proposed method utilizing a SonixOne machine and a Verasonics machine with an L9-4 array and an L11-5 array. We conducted two types of acquisitions to obtain calibration data: stable and free-hand, using two different calibration phantoms. Without the proposed method, the mean classification accuracy when applying a model on data acquired from one system to data acquired from another system was 50%, and the mean AUC was 0.405. With the proposed method, mean accuracy increased to 99%, and the AUC rose to the 0.999. Additional observations include the choice of the calibration phantom led to statistically significant changes in the performance of the proposed method. Moreover, robust implementation inspired by Wiener filtering provided an effective method for transferring the domain from one machine to another machine, and it can succeed using just a single calibration view. Lastly, the proposed method proved effective when a different transducer was used in the test machine.

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Domain Adaptation for Ultrasound Beamforming

Cited by 11 publications

References 26 publications

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

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

High-fidelity deconvolution for acoustic-resolution photoacoustic microscopy enabled by convolutional neural networks

Machine-to-Machine Transfer Function in Deep Learning-Based Quantitative Ultrasound

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