Text-prompted 3D CT segmentation. Name a structure, get its mask. 286 anatomical structures and lesions, 39M parameters, trained from scratch. ONLY PROMPT FOR THE STRUCTURES YOU NEED. Prompting for unnecessary or anatomically absent structures may cause hallucinations and false-positive segmentations. After inferece, DART-Seg may produce fragments. Use the below postprocessing to fix that. Quote prompts and paths that contain spaces, e.g. --prompts "left kidney" or --image "my scan.nii.gz". Python API (run from inside the downloaded folder so dartseg.py is importable): - Residual 3D U-Net; each prompt's text embedding attends to image features and produces its mask. All prompts share one…
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Koushik45048545309
Koushik45048545309
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Published tasks: Image segmentation.
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