Unknown

Dataset Information

0

Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks.

ABSTRACT: Instrument detection, pose estimation, and tracking in surgical videos are an important vision component for computer-assisted interventions. While significant advances have been made in recent years, articulation detection is still a major challenge. In this paper, we propose a deep neural network for articulated multi-instrument 2-D pose estimation, which is trained on detailed annotations of endoscopic and microscopic data sets. Our model is formed by a fully convolutional detection-regression network. Joints and associations between joint pairs in our instrument model are located by the detection subnetwork and are subsequently refined through a regression subnetwork. Based on the output from the model, the poses of the instruments are inferred using maximum bipartite graph matching. Our estimation framework is powered by deep learning techniques without any direct kinematic information from a robot. Our framework is tested on single-instrument RMIT data, and also on multi-instrument EndoVis and in vivo data with promising results. In addition, the data set annotations are publicly released along with our code and model.

SUBMITTER: Du X 

PROVIDER: S-EPMC6051486 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

Json Xml
altmetric image

Publications

Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks.

Du Xiaofei X   Kurmann Thomas T   Chang Ping-Lin PL   Allan Maximilian M   Ourselin Sebastien S   Sznitman Raphael R   Kelly John D JD   Stoyanov Danail D  

IEEE transactions on medical imaging 20180501 5

Instrument detection, pose estimation, and tracking in surgical videos are an important vision component for computer-assisted interventions. While significant advances have been made in recent years, articulation detection is still a major challenge. In this paper, we propose a deep neural network for articulated multi-instrument 2-D pose estimation, which is trained on detailed annotations of endoscopic and microscopic data sets. Our model is formed by a fully convolutional detection-regressio  ...[more]

Similar Datasets

Unknown WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans.
| S-EPMC8078761 | biostudies-literature
Unknown Fast animal pose estimation using deep neural networks.
| S-EPMC6899221 | biostudies-literature
Unknown Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks.
| S-EPMC5751073 | biostudies-literature
Unknown Automatic labeling of molecular biomarkers of immunohistochemistry images using fully convolutional networks.
| S-EPMC5774709 | biostudies-literature
Unknown Fetal Cortical Plate Segmentation Using Fully Convolutional Networks With Multiple Plane Aggregation.
| S-EPMC7738480 | biostudies-literature
Unknown Highly Accurate and Fully Automatic 3D Head Pose Estimation and Eye Gaze Estimation Using RGB-3D Sensors and 3D Morphable Models.
| S-EPMC6308695 | biostudies-other
Unknown Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks.
| S-EPMC6527327 | biostudies-literature
Unknown Spliceator: multi-species splice site prediction using convolutional neural networks
| S-EPMC8609763 | biostudies-literature
Unknown High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features.
| S-EPMC6157083 | biostudies-other
Unknown Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.
| S-EPMC6138894 | biostudies-other