Automatic Peripheral Nerve Segmentation in Presence of Multiple Annotators

Peripheral Nerve Blocking (PNB) is a technique commonly used to perform regional anesthesia. The success of PNB procedures lies of the accurate location of the target nerve. The ultrasound images (UI) have frequently been used aiming to locate nerve structures in the context of PNB procedures. This type of images allows a direct visualization of the target nerve, and the anatomical structures around it. Notwithstanding, the nerve segmentation in UI by an anesthesiologist is not straightforward since these images are affected by several artifacts; hence, the accuracy of nerve segmentation depends on the anesthesiologist expertise. In this sense, we face a scenario where we have manual multiple nerve segmentations performed by several anesthesiologists with different levels of expertise. In this paper, we propose a nerve segmentation approach based on supervised learning. For the classification step, we compare two schemes based on the concepts “Learning from crowds” aiming to code the information of multiple manual segmentations. Attained results show that our approach finds a suitable UI approximation by ensuring the identification of discriminative nerve patterns according to the opinions given by multiple specialists.