Bilateral Supervision Network for Semi-Supervised Medical Image Segmentation

Bilateral Supervision Network for Semi-Supervised Medical Image Segmentation

Research Background and Motivation Medical image segmentation is of great significance in the image analysis of anatomical structures and lesion areas, as well as in clinical diagnosis. However, existing fully supervised learning methods rely on a large amount of annotated data, and obtaining pixel-level annotated data for medical images is costly ...

3D/2D Vessel Registration Based on Monte Carlo Tree Search and Manifold Regularization

3D/2D Vessel Registration Based on Monte Carlo Tree Search and Manifold Regularization

Research on 3D/2D Vascular Registration Based on Monte Carlo Tree Search and Manifold Regularization In interventional vascular surgery, enhanced intraoperative real-time imaging technology can compensate for the shortcomings of DSA navigation, such as the lack of depth information and excessive use of toxic contrast agents, by projecting preoperat...

Exploration of Coincidence Detection of Cascade Photons to Enhance Preclinical Multi-Radionuclide SPECT Imaging

Exploration of Coincidence Detection of Cascade Photons to Enhance Preclinical Multi-Radionuclide SPECT Imaging

Exploration of Coincidence Detection of Cascade Photons to Improve Multi-Nuclide SPECT Imaging Radiopharmaceutical Therapy (RPT) has garnered increasing interest in recent years, especially in SPECT imaging involving the simultaneous use of multiple tracers. Traditional imaging methods are prone to scattering and crosstalk from different energy γ-r...

Whole Reconstruction-Free System Design for Direct Positron Emission Imaging from Image Generation to Attenuation Correction

Whole Reconstruction-Free System Design for Direct Positron Emission Imaging from Image Generation to Attenuation Correction

Background Introduction A hundred years ago, Hevesy first proposed using radioactive tracers as biological markers in plants, later validated through experiments in rats. This discovery propelled the development of nuclear medicine and molecular imaging in the biomedical field, making it possible to quantitatively visualize biological processes at ...

AI-based Denoising of Head Impact Kinematics Measurements with Convolutional Neural Network for Traumatic Brain Injury Prediction

Research and Application of Denoising Head Impact Kinematics Measurement Based on Convolutional Neural Networks Research Background Mild Traumatic Brain Injury (MTBI) is a global health threat. Humans often face the risk of MTBI in situations such as falls, traffic accidents, and sports. According to statistics, there were over 27 million brain inj...