2017 Robotic Instrument Segmentation Challenge

Abstract

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. However, this type of approach has had limited translation to problems in robotic assisted surgery as this field has never established the same level of common datasets and benchmarking methods. In 2015 a sub-challenge was introduced at the EndoVis workshop where a set of robotic images were provided with automatically generated annotations from robot forward kinematics. However, there were issues with this dataset due to the limited background variation, lack of complex motion and inaccuracies in the annotation. In this work we present the results of the 2017 challenge on robotic instrument segmentation which involved 10 teams participating in binary, parts and type based segmentation of articulated da Vinci robotic instruments.

References in corpus (6)

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• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
• Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning
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Cited by in corpus (24)

• 2018 Robotic Scene Segmentation Challenge
• SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction
• Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision
• Robust Medical Instrument Segmentation Challenge 2019
• AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery
• Multi-frame Feature Aggregation for Real-time Instrument Segmentation in Endoscopic Video
• OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images
• RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network
• Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction
• hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries
• ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery
• One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video
• BARNet: Bilinear Attention Network with Adaptive Receptive Fields for Surgical Instrument Segmentation
• LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images
• Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video
• How can we learn (more) from challenges? A statistical approach to driving future algorithm development
• E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception
• Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video
• Unsupervised Surgical Instrument Segmentation via Anchor Generation and Semantic Diffusion
• Learning Where to Look While Tracking Instruments in Robot-assisted Surgery
• Task Fingerprinting for Meta Learning in Biomedical Image Analysis
• Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments
• 2020 CATARACTS Semantic Segmentation Challenge
• Learning and Reasoning with the Graph Structure Representation in Robotic Surgery