Spinal Vertebrae Segmentation Dataset

Key Features:

• Comprehensive Imaging: The dataset includes 5 studies, captured from different angles, covering a range of spinal anomalies such as a focal mass in the Th7 vertebral body, fluid masses, and spondyloarthrosis.
• Clinical Annotations: All images are thoroughly labeled by experienced medical professionals and come with corresponding PDF reports, allowing for accurate diagnosis and reference.
• Diverse Conditions: The scans highlight both common and complex conditions like osteophytes, fluid masses on the anterior contour of the vertebrae, and dorsal protrusions of the Th11-Th12 disc. These features make it highly applicable for detecting fractures and classifying various spine-related abnormalities.

Applications in AI and Healthcare:

• Fracture Detection Models: This dataset is particularly useful for training deep learning algorithms to detect vertebral fractures, nerve compression, and other dystrophic changes.
• Segmentation and Classification: The detailed imaging of vertebrae, intervertebral discs, and surrounding tissues enables the segmentation of critical regions for AI-driven diagnostic tools.
• Training Data for Degenerative Disease Detection: With a focus on conditions like spondyloarthrosis, the dataset provides robust training material for detecting degenerative spinal diseases, enhancing the accuracy of automated systems.

Use Case for AI Training:

Researchers and healthcare professionals can leverage this dataset to develop machine learning models for improved diagnostics in spinal health, particularly focusing on dystrophic conditions and spine anomaly classification.