Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography
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Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography. / Bhattacharya, Debayan; Latus, Sarah; Behrendt, Finn; Thimm, Florin; Eggert, Dennis; Betz, Christian; Schlaefer, Alexander.
In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, Vol. 2023, 07.2023, p. 1-4.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography
AU - Bhattacharya, Debayan
AU - Latus, Sarah
AU - Behrendt, Finn
AU - Thimm, Florin
AU - Eggert, Dennis
AU - Betz, Christian
AU - Schlaefer, Alexander
PY - 2023/7
Y1 - 2023/7
N2 - Needle positioning is essential for various medical applications such as epidural anaesthesia. Physicians rely on their instincts while navigating the needle in epidural spaces. Thereby, identifying the tissue structures may be helpful to the physician as they can provide additional feedback in the needle insertion process. To this end, we propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We investigate the performance of the deep neural network in a limited labelled dataset scenario and propose a novel contrastive pretraining strategy that learns invariant representation for phase and intensity data. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84±0.10 whereas the model achieves an F1 score of 0.60±0.07 without it. Further, we analyse the importance of phase and intensity individually towards tissue classification.
AB - Needle positioning is essential for various medical applications such as epidural anaesthesia. Physicians rely on their instincts while navigating the needle in epidural spaces. Thereby, identifying the tissue structures may be helpful to the physician as they can provide additional feedback in the needle insertion process. To this end, we propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We investigate the performance of the deep neural network in a limited labelled dataset scenario and propose a novel contrastive pretraining strategy that learns invariant representation for phase and intensity data. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84±0.10 whereas the model achieves an F1 score of 0.60±0.07 without it. Further, we analyse the importance of phase and intensity individually towards tissue classification.
KW - Tomography, Optical Coherence
KW - Learning
KW - Anesthesia, Epidural
KW - Needles
KW - Neural Networks, Computer
U2 - 10.1109/EMBC40787.2023.10340648
DO - 10.1109/EMBC40787.2023.10340648
M3 - SCORING: Journal article
C2 - 38082740
VL - 2023
SP - 1
EP - 4
JO - Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
JF - Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
SN - 2375-7477
ER -
