arXiv AI
Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images
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TotalSynth: Robust Whole-Body Synthetic CT from MRI and CBCT
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Spectral Adapters for Segment Anything Model-based Segmentation of Colorectal Liver Metastases in Computed Tomography
The paper introduces two lightweight spectral adapters—Directional Spectral Adapter (DiSECT) and Spectral Instance-Guided Adapter (SiGA)—to adapt the Segment Anything Model (SAM) for accurate segmentation of colorectal liver metastases in contrast‑enhanced CT scans. SiGA achieves the highest single‑point Dice score of 0.77 and performs comparably to a 3D nnU‑Net baseline under no‑prompt inference, while DiSECT requires only 0.14 million trainable parameters. The study evaluates the adapters on 446 CT volumes across various prompting regimes, demonstrating that spectral adapters can efficiently adapt SAM with limited trainable parameters while maintaining strong segmentation accuracy.
arXiv AI
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Deep Learning Estimation of Sex, Age, Height, and Weight from CT-derived Digitally Reconstructed Radiographs
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arXiv AI
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Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes
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arXiv Computer Vision
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Medical Foundation Model Features as Perceptual Loss for Brain MRI Contrast Dose Simulation
arXiv:2608.28773v1 Announce Type: cross Abstract: Perceptual losses are widely used in medical image synthesis because they encourage agreement in high-level structure beyond voxel-wise intensity sim...
arXiv AI
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CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT
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