Nerve Localization by Machine Learning Framework with New Feature Selection Algorithm

The application of Ultrasound-Guided Regional Anesthesia (UGRA) is growing rapidly in medical field and becoming a standard procedure in many worldwide hospitals. UGRA practice requires a high training skill. Nerve detection is among the difficult tasks that anesthetists can meet in UGRA procedure. There is a need for automatic method to localize the nerve zone in ultrasound images, in order to assist anesthetists to better perform this procedure. On the other hand, the nerve detection in this type of images is a challenging task, since the noise and other artifacts corrupt visual properties of such tissue. In this paper, we propose a nerve localization framework with a new feature selection algorithm. The proposed method is based on several statistical approaches and learning models, taking advantage of each approach to increase performance. Results show that the proposed method can correctly and efficiently identify the nerve zone and outperforms the state-of-the-art techniques. It achieves

$$82\%$$

of accuracy (f-score index) on a first dataset (8 patients) and

$$61\%$$

on a second dataset (5 patients, acquired in different period of time and not used for training).