${1.46} \; \pm \; {1.11}$ mm,

${1.58} \; \pm \; {1.30}$

mm, and

${1.55} \; \pm \; {0.86}$

mm, respectively. In addition, the instance segmentation-based system simultaneously estimated sound speeds with absolute errors (mean ± one standard deviation) of

${19.22} \; \pm \; {26.26}$

m/s in simulated data and standard deviations ranging 14.6–32.3 m/s in experimental data. These results demonstrate the potential of the proposed photoacoustic imaging-based methods to localize and track tool tips in three dimensions during surgical and interventional procedures.">

Deep Learning to Localize Photoacoustic Sources in Three Dimensions: Theory and Implementation (original) (raw)

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