A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation

TL;DR

This paper introduces a large open-source ultrasound dataset of porcine spinal cords with injury annotations, benchmarks deep learning models for injury localization and anatomy segmentation, and evaluates their generalization to human data.

Contribution

It provides the first large publicly available spinal cord ultrasound dataset with benchmarks for deep learning models and assesses their zero-shot generalization to human images.

Findings

YOLOv8 achieved mAP50-95 of 0.606 for injury detection

DeepLabv3 achieved 0.587 Dice score on porcine anatomy

SAMed achieved 0.445 Dice score on human data

Abstract

While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,223 Brightness-mode (B-mode) images consisting of sagittal slices of porcine spinal cords (N=25) before and after a contusion injury. We additionally benchmark the performance metrics of several state-of-the-art object detection algorithms to localize the site of injury and semantic segmentation models to label the anatomy for comparison and creation of task-specific architectures. Finally, we evaluate the zero-shot generalization capabilities of the segmentation models on human ultrasound spinal cord images to determine whether training on our porcine dataset is sufficient for accurately interpreting human data. Our results show that the YOLOv8 detection model outperforms all evaluated models for injury localization, achieving a mean Average Precision (mAP50-95) score of 0.606. Segmentation metrics indicate that the DeepLabv3 segmentation model achieves the highest accuracy on unseen porcine anatomy, with a Mean Dice score of 0.587, while SAMed achieves the highest Mean Dice score generalizing to human anatomy (0.445). To the best of our knowledge, this is the largest annotated dataset of spinal cord ultrasound images made publicly available to researchers and medical professionals, as well as the first public report of object detection and segmentation architectures to assess anatomical markers in the spinal cord for methodology development and clinical applications.