Automatic fiducial localization in ultrasound images for a thermal ablation validation platform

Bartha, Laura; Lassó, András; Chen, Thomas Kuiran; Fichtinger, Gabor

doi:10.1117/12.878568

Cited by 4 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2 b . While an automatic approach to segmenting the reflections of the thin wires has been implemented [8], this approach relies on the standard and known geometry of a Z ‐phantom, as the image always contains three co‐linear reflections [8]. Furthermore, the GUSCAL approach involves inserting the needle at an oblique angle such that it fully intersects the US image plane, producing a more amplified reflection compared to the thin wires.…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach for automatic out‐of‐plane needle localisation for semi‐automatic ultrasound probe calibration

Groves

VanBerlo

Peters

et al. 2019

Healthcare Technology Letters

View full text Add to dashboard Cite

The authors present a deep learning algorithm for the automatic centroid localisation of out-of-plane US needle reflections to produce a semi-automatic ultrasound (US) probe calibration algorithm. A convolutional neural network was trained on a dataset of 3825 images at a 6 cm imaging depth to predict the position of the centroid of a needle reflection. Applying the automatic centroid localisation algorithm to a test set of 614 annotated images produced a root mean squared error of 0.62 and 0.74 mm (6.08 and 7.62 pixels) in the axial and lateral directions, respectively. The mean absolute errors associated with the test set were 0.50 ± 0.40 mm and 0.51 ± 0.54 mm (4.9 ± 3.96 pixels and 5.24 ± 5.52 pixels) for the axial and lateral directions, respectively. The trained model was able to produce visually validated US probe calibrations at imaging depths on the range of 4-8 cm, despite being solely trained at 6 cm. This work has automated the pixel localisation required for the guided-US calibration algorithm producing a semi-automatic implementation available open-source through 3D Slicer. The automatic needle centroid localisation improves the usability of the algorithm and has the potential to decrease the fiducial localisation and target registration errors associated with the guided-US calibration method.

show abstract

Section: Introductionmentioning

confidence: 99%