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.
Nature methods , volume=
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A frequency-enhanced Vision Transformer with FDSA, FGMLP, WAFF, and FCSB modules delivers superior volumetric medical image segmentation performance and efficiency over prior state-of-the-art methods.
Vesselpose predicts voxel-wise direction vectors to extend the TEASAR algorithm for topologically accurate vascular graph reconstruction from 3D images.
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.
nnU-Net with ResNet encoder, intensity normalization, batch dice loss, and CraveMix augmentation reaches Dice 0.80 and third place in AutoPET III.
citing papers explorer
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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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FEFormer: Frequency-enhanced Vision Transformer for Generic Knowledge Extraction and Adaptive Feature Fusion in Volumetric Medical Image Segmentation
A frequency-enhanced Vision Transformer with FDSA, FGMLP, WAFF, and FCSB modules delivers superior volumetric medical image segmentation performance and efficiency over prior state-of-the-art methods.
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Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images
Vesselpose predicts voxel-wise direction vectors to extend the TEASAR algorithm for topologically accurate vascular graph reconstruction from 3D images.
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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.
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Advanced Tumor Segmentation in PET/CT Imaging: A Training Strategy Study with nnU-Net for AutoPET III
nnU-Net with ResNet encoder, intensity normalization, batch dice loss, and CraveMix augmentation reaches Dice 0.80 and third place in AutoPET III.