# Deep Learning-Based Segmentation of the Ulnar Nerve in Ultrasound Images

## TL;DR

This paper evaluates deep learning methods for segmenting the ulnar nerve in ultrasound images using a large dataset and compares different models and data augmentation techniques.

## Contribution

The study introduces the first large dataset of ulnar nerve ultrasound images and provides insights into effective data augmentation and model selection for nerve segmentation.

## Key findings

- Shear, rotate, and resize data augmentations significantly improved segmentation performance with p-values < 0.05.
- Traditional U-Net models outperformed newer architectures with a Dice score of 0.88 and IoU of 0.81.

## Abstract

Background and Objectives: We evaluate deep learning-based segmentation methods for detecting the ulnar nerve in ultrasound (US) images, leveraging the first-ever large US dataset of the ulnar nerve. We compare several widely used segmentation models, analyze their performance, and evaluate several common data augmentation techniques for the US. Materials and Methods: Our analysis is conducted on a large dataset of 4789 US images from 545 patients, with expert-annotated ground-truth segmentations of the ulnar nerve, and uses six segmentation models with several backbone architectures. Further, we analyze the statistical significance of five common data augmentation techniques on segmentation performance: flipping, rotation, shearing, contrast and brightness adjustments, and resizing. Results: In this study, the shear, rotate, and resize augmentations consistently improved segmentation performance across multiple runs, with p-values < 0.05 in a paired t-test relative to the no-augmentation baseline. Furthermore, we showed that newer architectures do not provide any metric improvements over traditional U-Net models, which achieved a Dice score of 0.88 and an IoU of 0.81. Conclusions: Through our systematic analysis of segmentation models and data augmentation strategies, we provide key insights into optimizing deep learning approaches for ulnar nerve segmentation and other US-based nerve segmentation tasks.