rachitsaluja/BraTS-2023-Metrics
Official BraTS 2023 Segmentation Performance Metrics
This tool helps radiologists, neurologists, and medical researchers more accurately assess how well AI models segment brain tumors from MRI scans. It takes in predicted tumor segmentations from an AI model and compares them against known ground truth segmentations, outputting detailed, lesion-by-lesion performance scores (like Dice and Hausdorff distance) that highlight missed tumors or false positives. This provides a more clinically relevant evaluation than traditional image-level metrics, especially for multi-lesion cases.
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Use this if you need to rigorously evaluate the performance of brain tumor segmentation models, focusing on individual tumor detection and accuracy, rather than just overall image-level correctness.
Not ideal if you are evaluating segmentation models for pathologies outside of brain tumors or if your primary goal is a quick, high-level assessment of image-wide segmentation accuracy.
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Jan 11, 2024
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