Lumbar Coordinate Pretraining Dataset

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Lumbar Coordinate Pretraining Dataset

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Lumbar Coordinate Pretraining Dataset

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Lumbar Coordinate Pretraining Dataset

Description

Discover the LEGO Minifigure Faces dataset, a curated collection of 800 annotated images for facial recognition tasks.Explore the RSNA 2024 Lumbar Spine Degenerative Classification dataset, featuring annotated medical images of the lower lumbar spine with key points and degenerative changes classification, ideal for pretraining neural networks and advancing medical imaging research

Description:

This dataset was curated as a pretraining objective for the RSNA 2024 Lumbar Spine Degenerative Classification competition. It comprises a comprehensive collection of medical images focusing on the lower lumbar spine. Each image within the dataset is annotated with five key points, corresponding to the five lower lumbar vertebrae (L1 to L5).

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Dataset Composition

The dataset includes:

X-ray Images: High-resolution radiographs providing clear views of the lumbar spine, allowing for detailed analysis of vertebral alignment, disc spaces, and potential degenerative changes.
MRI Scans: Detailed magnetic resonance images that offer insights into soft tissue conditions, including intervertebral discs, spinal cord, and nerve roots.
CT Scans: Computed tomography images highlighting bone structures and aiding in the detection of fractures, bone spurs, and other skeletal abnormalities.

Key Features

Annotated Key Points: Each image is precisely annotated with five key points marking the locations of the L1 to L5 vertebrae. These annotations are crucial for developing and validating algorithms for automated vertebral detection and classification.
Degenerative Changes Classification: The dataset includes labels indicating various stages of lumbar spine degeneration, such as disc herniation, spinal stenosis, and spondylolisthesis. These classifications help in training models to recognize and categorize degenerative changes.
Clinical Metadata: Accompanying each image is clinical metadata, including patient age, gender, and clinical symptoms. This data enriches the dataset, enabling the development of more robust and context-aware predictive models.