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SAM-Med3D [Paper]

🌟 Highlights

📚 Curated the most extensive volumetric medical dataset to date for training, boasting 131K 3D masks and 247 categories.

🚤 Achieved efficient promptable segmentation, requiring 10 to 100 times fewer prompt points for satisfactory 3D outcomes.

🏆 Conducted a thorough assessment of SAM-Med3D across 15 frequently-used v

uni-medical • GitHub - uni-medical/SAM-Med3D: SAM-Med3D: An Efficient 3D Model for Promptable Volumetric Medical Image Segmentation

SAM 2 Released: 3D Medical Image Segmentation Solved? I captured a screen capture of my fast scrolling through a random dataset in OHIF and fed it to SAM2. The result is almost flawless. https://t.co/XjQdGgOtdQ... See more

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AR Breakthrough! This technology turns 2D MRI and CT scans into real-time 3D visuals, overlaying them onto the patient’s body ( 🎥: medivis_ar)

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