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Found 57 papers, 15 papers with code
Bimodal Camera Pose Prediction for Endoscopy
Deducing the 3D structure of endoscopic scenes from images remains extremely challenging.
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
Task-Aware Active Learning for Endoscopic Image Analysis
Semantic segmentation of polyps and depth estimation are two important research problems in endoscopic image analysis.
Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation
We employed our method on two powerful and widely used semantic segmentation networks: UNet and U2Net to train in a multi-task setup.
A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its effect on Image Correspondence
In this paper, we aim at removing specular highlights from endoscopic videos using machine learning.
Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation
Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre.
3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces
Learning a disentangled, interpretable, and structured latent representation in 3D generative models of faces and bodies is still an open problem.
2020 CATARACTS Semantic Segmentation Challenge
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes
To achieve a reproducible and accurate measurement, a sonographer needs to identify three standard 2D planes of the fetal anatomy (head, abdomen, femur) and manually mark the key anatomical landmarks on the image for accurate biometry and fetal weight estimation.
FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset
Through the \textit{Fetoscopic Placental Vessel Segmentation and Registration (FetReg)} challenge, we present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
Autonomous object harvesting using synchronized optoelectronic microrobots
Optoelectronic tweezer-driven microrobots (OETdMs) are a versatile micromanipulation technology based on the use of light induced dielectrophoresis to move small dielectric structures (microrobots) across a photoconductive substrate.
Surgical Visual Domain Adaptation: Results from the MICCAI 2020 SurgVisDom Challenge
Surgical data science is revolutionizing minimally invasive surgery by enabling context-aware applications.
Intrapapillary Capillary Loop Classification in Magnification Endoscopy: Open Dataset and Baseline Methodology
A future work path of high clinical relevance is the extension of the classification to ESCN types.
Image Compositing for Segmentation of Surgical Tools without Manual Annotations
Background data is captured by collecting videos that do not contain instruments.
Gesture Recognition in Robotic Surgery: a Review
This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions.
Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks
We additionally use Shape from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images.
Stereo Correspondence and Reconstruction of Endoscopic Data Challenge
The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China.
SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction
The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths with coverage over the majority of the endoscopic images.
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.
Detecting small polyps using a Dynamic SSD-GAN
Endoscopic examinations are used to inspect the throat, stomach and bowel for polyps which could develop into cancer.
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies.
Surgical Video Motion Magnification with Suppression of Instrument Artefacts
Video motion magnification could directly highlight subsurface blood vessels in endoscopic video in order to prevent inadvertent damage and bleeding.
Intraoperative Liver Surface Completion with Graph Convolutional VAE
In this work we propose a method based on geometric deep learning to predict the complete surface of the liver, given a partial point cloud of the organ obtained during the surgical laparoscopic procedure.
Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking
The latter, a combination of deep learning with optical flow tracking, yields an average balanced accuracy of 78. 2% across all the validated datasets.
Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings
Even when this is a known scene, the answer typically requires an expensive search across scale space, with matching and geometric verification of large sets of local features.
Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery
Semantic tool segmentation in surgical videos is important for surgical scene understanding and computer-assisted interventions as well as for the development of robotic automation.
Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos.
The challenges of deploying artificial intelligence models in a rapidly evolving pandemic
The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks.
Deep iterative vessel segmentation in OCT angiography
This paper addresses retinal vessel segmentation on optical coherence tomography angiography (OCT-A) images of the human retina.
Multi-Task Recurrent Neural Network for Surgical Gesture Recognition and Progress Prediction
Surgical gesture recognition is important for surgical data science and computer-aided intervention.
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation
Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications.
Weakly Supervised Recognition of Surgical Gestures
Hence, the potential of weak supervision could be to improve unsupervised learning while avoiding manual annotation of large datasets.
Whole-Sample Mapping of Cancerous and Benign Tissue Properties
Structural and mechanical differences between cancerous and healthy tissue give rise to variations in macroscopic properties such as visual appearance and elastic modulus that show promise as signatures for early cancer detection.
Deep Sequential Mosaicking of Fetoscopic Videos
Mosaicking can align multiple overlapping images to generate an image with increased FoV, however, existing techniques apply poorly to fetoscopy due to the low visual quality, texture paucity, and hence fail in longer sequences due to the drift accumulated over time.
CaDIS: Cataract Dataset for Image Segmentation
Video feedback provides a wealth of information about surgical procedures and is the main sensory cue for surgeons.
2D Semantic Segmentation task 1 (8 classes)
2D Semantic Segmentation task 2 (17 classes)
+3
2017 Robotic Instrument Segmentation Challenge
In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison.
Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation
Accounting for 26% of all new cancer cases worldwide, breast cancer remains the most common form of cancer in women.
SurReal: enhancing Surgical simulation Realism using style transfer
Surgical simulation is an increasingly important element of surgical education.
FaceOff: Anonymizing Videos in the Operating Rooms
After testing an existing face detection trained model, a new dataset tailored to the surgical environment, with faces obstructed by surgical masks and caps, was collected for fine-tuning to achieve higher face-detection rates in the OR.
Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks
Transoesophageal echocardiography (TEE) is a valuable diagnostic and monitoring imaging modality.
Higher Order of Motion Magnification for Vessel Localisation in Surgical Video
Locating vessels during surgery is critical for avoiding inadvertent damage, yet vasculature can be difficult to identify.
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods.
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
The paper presents a comparative validation study of different vision-based methods for instrument segmentation and tracking in the context of robotic as well as conventional laparoscopic surgery.
Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia
We present a new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis.
Augmented Reality needle ablation guidance tool for Irreversible Electroporation in the pancreas
Irreversible electroporation (IRE) is a soft tissue ablation technique suitable for treatment of inoperable tumours in the pancreas.
Widening siamese architectures for stereo matching
Computational stereo is one of the classical problems in computer vision.
Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery
An additional advantage of our approach is that instrument detection at test time is achieved while avoiding the need for scale-dependent sliding window evaluation.
Refractive Structure-From-Motion Through a Flat Refractive Interface
By explicitly considering a refractive interface, we develop a succinct derivation of the refractive fundamental matrix in the form of the generalised epipolar constraint for an axial camera.
Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking
Tracking-by-detection algorithms are widely used for visual tracking, where the problem is treated as a classification task where an object model is updated over time using online learning techniques.
ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools
We propose the use of parametric rectified linear units for semantic labeling in these small architectures to increase the regularization ability of the design and maintain the segmentation accuracy without overfitting the training sets.
Fast Estimation of Haemoglobin Concentration in Tissue Via Wavelet Decomposition
Tissue oxygenation and perfusion can be an indicator for organ viability during minimally invasive surgery, for example allowing real-time assessment of tissue perfusion and oxygen saturation.
A comparative study of breast surface reconstruction for aesthetic outcome assessment
Breast cancer is the most prevalent cancer type in women, and while its survival rate is generally high the aesthetic outcome is an increasingly important factor when evaluating different treatment alternatives.
Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging
Intra-operative measurements of tissue shape and multi/ hyperspectral information have the potential to provide surgical guidance and decision making support.
Similarity Registration Problems for 2D/3D Ultrasound Calibration
The needle is tracked as a 3D line, and is scanned by the ultrasound as either a 3D line (3D US) or as a 2D point (2D US).
Inference of Haemoglobin Concentration From Stereo RGB
We demonstrate by using the co-registered stereo image data from two cameras it is possible to get robust SO2 estimation as well.
Probe-based Rapid Hybrid Hyperspectral and Tissue Surface Imaging Aided by Fully Convolutional Networks
In HSI mode standard endoscopic illumination is used, with the fibre probe collecting reflected light and encoding the spatial information into a linear format that can be imaged onto the slit of a spectrograph.
