SAM 3 outperforms SAM 2 under click prompting for zero-shot 3D medical segmentation across 16 datasets and 54 structures, with fewer failure modes in prompt-frame over-segmentation and prediction retention.
Totalsegmentator mri: Robust sequence- independent segmentation of multiple anatomic structures in mri.arXiv preprint arXiv:2405.19492, 2024
4 Pith papers cite this work. Polarity classification is still indexing.
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SynthRAD2025 shows deep learning produces synthetic CTs with MAE 48-65 HU and high dosimetric gamma passing rates for radiotherapy, performing better on CBCT-to-CT than MRI-to-CT tasks.
MedGS extends Gaussian Splatting with a relightable model tailored to endoscopic imaging where light and camera are co-located, achieving better novel-view synthesis and tissue editing than baselines.
A self-supervised approach uses consistent spatial relationships of anatomical structures across patients to improve 3D multi-modal medical image representations, yielding modest gains on segmentation and classification tasks.
citing papers explorer
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Comparing SAM 2 and SAM 3 for Zero-Shot Segmentation of 3D Medical Data
SAM 3 outperforms SAM 2 under click prompting for zero-shot 3D medical segmentation across 16 datasets and 54 structures, with fewer failure modes in prompt-frame over-segmentation and prediction retention.
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Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report
SynthRAD2025 shows deep learning produces synthetic CTs with MAE 48-65 HU and high dosimetric gamma passing rates for radiotherapy, performing better on CBCT-to-CT than MRI-to-CT tasks.
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MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging
MedGS extends Gaussian Splatting with a relightable model tailored to endoscopic imaging where light and camera are co-located, achieving better novel-view synthesis and tissue editing than baselines.
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Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging
A self-supervised approach uses consistent spatial relationships of anatomical structures across patients to improve 3D multi-modal medical image representations, yielding modest gains on segmentation and classification tasks.