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Found 5 papers, 1 papers with code
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection
This work intends to, first, propose a robust inpainting model to learn the details of healthy anatomies and reconstruct high-resolution images by preserving anatomical constraints.
Self-Supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET
Dynamic positron emission tomography imaging (dPET) provides temporally resolved images of a tracer enabling a quantitative measure of physiological processes.
Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine
We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning
The framework uses the patient CT scan and the fractured vertebra label to build a virtual healthy spine using a high-level approach.
