Advancements in Medical Imaging and Interventional Navigation

The field of medical imaging and interventional navigation is moving towards more accurate and efficient methods for 3D reconstruction, registration, and segmentation. Recent developments have focused on leveraging deep learning techniques, such as vision transformers and neural networks, to improve the precision and robustness of these methods. Additionally, there is a growing interest in developing frameworks that can handle complex anatomical structures, such as blood vessels and spine tissue, and that can provide real-time feedback for interventional procedures. Noteworthy papers include:

  • ZeroReg3D, a zero-shot registration pipeline for 3D consecutive histopathology image reconstruction, which achieves accurate 3D reconstruction without requiring large-scale training data.
  • Patch2Loc, an unsupervised approach for brain lesion detection that learns to localize patches in structural MRI and detects abnormal patches through location prediction errors.
  • Calibrated self-supervised vision transformers for intracranial arterial calcification segmentation, which outperform supervised methods and demonstrate improved robustness to varying slice thicknesses.

Sources

Real-Time 3D Guidewire Reconstruction from Intraoperative DSA Images for Robot-Assisted Endovascular Interventions

ZeroReg3D: A Zero-shot Registration Pipeline for 3D Consecutive Histopathology Image Reconstruction

Robust and Accurate Multi-view 2D/3D Image Registration with Differentiable X-ray Rendering and Dual Cross-view Constraints

Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection

Towards Markerless Intraoperative Tracking of Deformable Spine Tissue

Calibrated Self-supervised Vision Transformers Improve Intracranial Arterial Calcification Segmentation from Clinical CT Head Scans

Modality Agnostic, patient-specific digital twins modeling temporally varying digestive motion

Two-Steps Neural Networks for an Automated Cerebrovascular Landmark Detection

Parametric shape models for vessels learned from segmentations via differentiable voxelization

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