ArticleIEEE transactions on bio-medical engineering2026
In Vivo Demonstration of Deep Learning-Based Photoacoustic Visual Servoing System.
Article in IEEE transactions on bio-medical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Tracking performance and surgeon assessment of photoacoustic-based surgical guidance methods.International journal of computer assisted radiology and surgery · 2026Article
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9 authors.
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Abstract
objectiveTo develop the first known deep learning-based photoacoustic visual servoing system utilizing point source localization and hybrid position-force control to track catheter tips in three dimensions in real-time.
methodsWe integrated either object detection or instance segmentation-based localization with hybrid position-force control to create our novel system. Cardiac catheter tips were then tracked across distances of 40 mm in a plastisol phantom and 25-64 mm in an in vivo swine in real-time in nine visual servoing trials total.
resultsObject detection-based localization identified the cardiac catheter tip in 88.0-91.7% and 66.7-70.4% of phantom and in vivo channel data frames, respectively. Instance segmentation detection rates ranged 86.4-100.0% in vivo. These catheter tips were tracked with errors as low as 0.5 mm in phantom trials and 0.8 mm in the in vivo trials. The mean inference times were $\geq$ 145.3 ms and $\geq$ 516.3 ms with object detection-based and instance segmentation-based point source localization, respectively. The hybrid position-force control system enabled contact with the imaging surface during $\geq$99.43% of each visual servoing trial.
conclusionOur novel deep learning-based photoacoustic visual servoing system was successfully demonstrated. Object detection-based localization operated with inference times that are more suitable for real-time implementations while instance segmentation had lower tracking errors. SIGNIFICANCE: After implementing suggested optimization modifications, our novel system has the potential to track catheter tips, needle tips, and other surgical tool tips in real-time during surgical and interventional procedures.
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