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SAMRI-3D adapts SAM2 for 3D MRI segmentation, outperforming prior models

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced SAMRI-3D, a new benchmark and method for 3D MRI segmentation that adapts the Segment Anything Model 2 (SAM2). This approach significantly improves segmentation accuracy compared to previous SAM-based medical models, achieving a mean Dice score of 0.76 by fine-tuning only the decoder and memory modules. The method also introduces Global Volume Tokens (GVT) with a Truncated Signed Distance Field (TSDF) objective to better handle invisible boundaries in MRI scans, resulting in an overall accuracy of 0.78 with minimal variance across diverse datasets. AI

IMPACT Enhances medical imaging segmentation capabilities, potentially improving diagnostic accuracy and efficiency in radiology.

RANK_REASON The cluster describes a new research paper introducing a novel method and benchmark for 3D MRI segmentation.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SAMRI-3D adapts SAM2 for 3D MRI segmentation, outperforming prior models

COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    SAMRI-3D: Adapting SAM2 for 3D MRI Segmentation with Global Volume Tokens

    Foundation models such as Segment Anything Model 2 (SAM2) have transformed natural-image and video segmentation, and recent work has begun adapting them to medical imaging. These adaptations, however, are largely general-purpose models that treat MRI as one modality among many; l…

  2. arXiv cs.CV TIER_1 English(EN) · Zhao Wang, Wei Dai, Hongfu Sun, Craig Engstrom, Shekhar S. Chandra ·

    SAMRI-3D: Adapting SAM2 for 3D MRI Segmentation with Global Volume Tokens

    arXiv:2607.18014v1 Announce Type: new Abstract: Foundation models such as Segment Anything Model 2 (SAM2) have transformed natural-image and video segmentation, and recent work has begun adapting them to medical imaging. These adaptations, however, are largely general-purpose mod…

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