Spatio-Temporal Deep Learning Models for Tip Force Estimation During Needle Insertion

no code implementations • 22 May 2019 • Nils Gessert, Torben Priegnitz, Thore Saathoff, Sven-Thomas Antoni, David Meyer, Moritz Franz Hamann, Klaus-Peter Jünemann, Christoph Otte, Alexander Schlaefer

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

no code implementations • 30 May 2018 • Nils Gessert, Torben Priegnitz, Thore Saathoff, Sven-Thomas Antoni, David Meyer, Moritz Franz Hamann, Klaus-Peter Jünemann, Christoph Otte, Alexander Schlaefer

Needle insertion is common during minimally invasive interventions such as biopsy or brachytherapy.