# Deep learning with spatiotemporal consistency for nerve segmentation in ultrasound images

arXiv: https://arxiv.org/abs/1706.05870 · HTML: https://mlsanity.com/paper/1706.05870/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2017.

## Built on by (most cited first, 5 of 5)

- [An interpretable object detection based model for the diagnosis of neonatal lung diseases using Ultrasound images](https://mlsanity.com/paper/2105.10081/) (2021)
- [Transesophageal Echocardiography Generation using Anatomical Models](https://mlsanity.com/paper/2410.06781/) (2024)
- [Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks](https://mlsanity.com/paper/2110.06367/) (2021)
- [Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, & Future](https://mlsanity.com/paper/2103.14969/) (2021)
- [HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images](https://mlsanity.com/paper/2204.06697/) (2022)

## Abstract (arXiv)

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.