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Found 14 papers, 3 papers with code
SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation
Recently, more research has focused on the development of marker-less methods based on deep learning.
Image Synthesis with Class-Aware Semantic Diffusion Models for Surgical Scene Segmentation
These maps are then used by CASDM to generate surgical scene images, enhancing datasets for training and validating segmentation models.
Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis
Although artificial intelligence (AI)-based solutions have been devised to automate the dietary assessment process, these prior AI methodologies encounter challenges in their ability to generalize across a diverse range of food types, dietary behaviors, and cultural contexts.
Detecting the Sensing Area of A Laparoscopic Probe in Minimally Invasive Cancer Surgery
In surgical oncology, it is challenging for surgeons to identify lymph nodes and completely resect cancer even with pre-operative imaging systems like PET and CT, because of the lack of reliable intraoperative visualization tools.
Identifying Visible Tissue in Intraoperative Ultrasound Images during Brain Surgery: A Method and Application
In this paper, we propose a method for the identification of the visible tissue, which enables the analysis of ultrasound probe and tissue contact via the detection of acoustic shadow and construction of confidence maps of the perceptual salience.
SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery
This assessment uses benchmarking metrics that were purposely developed for this challenge, to verify the efficacy of unsupervised deep learning algorithms in tracking soft-tissue.
Collaborative Robotic Ultrasound Tissue Scanning for Surgical Resection Guidance in Neurosurgery
A key application of the proposed platform is the scanning of brain tissue to guide tumour resection.
Regularizing disparity estimation via multi task learning with structured light reconstruction
Secondly, we \textcolor{black}{explore the use of a multi task learning (MTL) framework for the joint training of structured light and disparity.
Self-Supervised Depth Estimation in Laparoscopic Image using 3D Geometric Consistency
Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system.
Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images
Dense depth estimation and 3D reconstruction of a surgical scene are crucial steps in computer assisted surgery.
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry
To enforce the epipolar constraint, the mutual epipolar attention mechanism has been designed which gives more emphasis to correspondences of features which lie on the same epipolar line while learning mutual information between the input stereo pair.
Ranked #1 on
Depth Prediction
on KITTI 2015
Surgical Data Science -- from Concepts toward Clinical Translation
We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation
The 3D structure of the surgical scene is recovered and a feature-based method is proposed to estimate the motion of the tissue in real-time.
Real-time 3D Tracking of Articulated Tools for Robotic Surgery
The proposed method is based on the CAD model of the tools as well as robot kinematics to generate online part-based templates for efficient 2D matching and 3D pose estimation.
