The field of medical image analysis and surgical guidance is rapidly evolving, with a focus on developing innovative methods for image segmentation, registration, and visualization. Recent research has explored the use of deep learning techniques, such as transformers and convolutional neural networks, to improve the accuracy and efficiency of medical image analysis. Additionally, there is a growing interest in developing markerless augmented reality registration pipelines for surgical guidance, which have shown promising results in clinical settings. Noteworthy papers in this area include MambaNetLK, which achieved state-of-the-art performance in 3D point cloud registration, and UKAST, which introduced a novel transformer architecture for medical image segmentation. These advancements have the potential to significantly improve the accuracy and effectiveness of medical image analysis and surgical guidance, leading to better patient outcomes.
Advancements in Medical Image Analysis and Surgical Guidance
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A filtering scheme for confocal laser endomicroscopy (CLE)-video sequences for self-supervised learning
MambaNetLK: Enhancing Colonoscopy Point Cloud Registration with Mamba
TA-LSDiff:Topology-Aware Diffusion Guided by a Level Set Energy for Pancreas Segmentation
MicroAUNet: Boundary-Enhanced Multi-scale Fusion with Knowledge Distillation for Colonoscopy Polyp Image Segmentation
RDTE-UNet: A Boundary and Detail Aware UNet for Precise Medical Image Segmentation
Lite ENSAM: a lightweight cancer segmentation model for 3D Computed Tomography
Markerless Augmented Reality Registration for Surgical Guidance: A Multi-Anatomy Clinical Accuracy Study
Learning Spatial Awareness for Laparoscopic Surgery with AI Assisted Visual Feedback
Monocular absolute depth estimation from endoscopy via domain-invariant feature learning and latent consistency
When Swin Transformer Meets KANs: An Improved Transformer Architecture for Medical Image Segmentation
Vitessce Link: A Mixed Reality and 2D Display Hybrid Approach for Visual Analysis of 3D Tissue Maps
Submanifold Sparse Convolutional Networks for Automated 3D Segmentation of Kidneys and Kidney Tumours in Computed Tomography