Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art
T Rueckert, D Rueckert, C Palm - Computers in Biology and Medicine, 2024 - Elsevier
In the field of computer-and robot-assisted minimally invasive surgery, enormous progress
has been made in recent years based on the recognition of surgical instruments in …
has been made in recent years based on the recognition of surgical instruments in …
Sam meets robotic surgery: an empirical study on generalization, robustness and adaptation
Abstract The Segment Anything Model (SAM) serves as a fundamental model for semantic
segmentation and demonstrates remarkable generalization capabilities across a wide range …
segmentation and demonstrates remarkable generalization capabilities across a wide range …
Transformer-based disease identification for small-scale imbalanced capsule endoscopy dataset
Vision Transformer (ViT) is emerging as a new leader in computer vision with its outstanding
performance in many tasks (eg, ImageNet-22k, JFT-300M). However, the success of ViT …
performance in many tasks (eg, ImageNet-22k, JFT-300M). However, the success of ViT …
Video-instrument synergistic network for referring video instrument segmentation in robotic surgery
Surgical instrument segmentation is fundamentally important for facilitating cognitive
intelligence in robot-assisted surgery. Although existing methods have achieved accurate …
intelligence in robot-assisted surgery. Although existing methods have achieved accurate …
Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images
Transformer-based methods have led to the revolutionizing of multiple computer vision
tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced …
tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced …
[HTML][HTML] Digital twins as a unifying framework for surgical data science: the enabling role of geometric scene understanding
Surgical data science is devoted to enhancing the quality, safety, and efficacy of
interventional healthcare. While the use of powerful machine learning algorithms is …
interventional healthcare. While the use of powerful machine learning algorithms is …
Curriculum-based augmented fourier domain adaptation for robust medical image segmentation
Accurate and robust medical image segmentation is fundamental and crucial for enhancing
the autonomy of computer-aided diagnosis and intervention systems. Medical data …
the autonomy of computer-aided diagnosis and intervention systems. Medical data …
Sam 2 in robotic surgery: An empirical evaluation for robustness and generalization in surgical video segmentation
The recent Segment Anything Model (SAM) 2 has demonstrated remarkable foundational
competence in semantic segmentation, with its memory mechanism and mask decoder …
competence in semantic segmentation, with its memory mechanism and mask decoder …
SME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-Supervised Polyp Segmentation
Fully-supervised polyp segmentation has accomplished significant triumphs over the years
in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions from …
in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions from …
Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models
Accurate identification and localization of anatomical structures of varying size and
appearance in laparoscopic imaging are necessary to leverage the potential of computer …
appearance in laparoscopic imaging are necessary to leverage the potential of computer …