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Spatio-Temporal Deep Learning Models for Tip Force Estimation During Needle Insertion
Our novel convGRU-CNN architecture results in the lowest mean absolute error of 1. 59 +- 1. 3 mN and a cross-correlation coefficient of 0. 9997, and clearly outperforms the other methods.
Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture
Needle insertion is common during minimally invasive interventions such as biopsy or brachytherapy.
Force Estimation from OCT Volumes using 3D CNNs
Our novel Siamese 3D CNN architecture outperforms single-path methods that achieve a mean average error of 11. 59 +- 6. 7 mN.
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