Deep learning with spatiotemporal consistency for nerve segmentation in ultrasound images
This addresses the challenge of nerve detection for anesthetists, potentially improving anesthesia delivery, but appears incremental as it combines existing techniques like CNNs and active contour models.
The paper tackles the problem of nerve segmentation in ultrasound images for Ultrasound-Guided Regional Anesthesia by proposing a deep learning method with spatiotemporal consistency, achieving robust segmentation results.
Ultrasound-Guided Regional Anesthesia (UGRA) has been gaining importance in the last few years, offering numerous advantages over alternative methods of nerve localization (neurostimulation or paraesthesia). However, nerve detection is one of the most tasks that anaesthetists can encounter in the UGRA procedure. Computer aided system that can detect automatically region of nerve, would help practitioner to concentrate more in anaesthetic delivery. In this paper we propose a new method based on deep learning combined with spatiotemporal information to robustly segment the nerve region. The proposed method is based on two phases, localisation and segmentation. The first phase, consists in using convolutional neural network combined with spatial and temporal consistency to detect the nerve zone. The second phase utilises active contour model to delineate the region of interest. Obtained results show the validity of the proposed approach and its robustness.