A Kinematic Bottleneck Approach For Pose Regression of Flexible Surgical Instruments directly from Images

• URL: http://arxiv.org/abs/2103.00586v1
• Date: Sun, 28 Feb 2021 18:41:18 GMT
• Title: A Kinematic Bottleneck Approach For Pose Regression of Flexible Surgical Instruments directly from Images
• Authors: Luca Sestini, Benoit Rosa, Elena De Momi, Giancarlo Ferrigno and Nicolas Padoy
• Abstract summary: We propose a self-supervised image-based method, exploiting, at training time only, the kinematic information provided by the robot. In order to avoid introducing time-consuming manual annotations, the problem is formulated as an auto-encoder. Validation of the method was performed on semi-synthetic, phantom and in-vivo datasets, obtained using a flexible robotized endoscope.
• Score: 17.32860829016479
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 3-D pose estimation of instruments is a crucial step towards automatic scene understanding in robotic minimally invasive surgery. Although robotic systems can potentially directly provide joint values, this information is not commonly exploited inside the operating room, due to its possible unreliability, limited access and the time-consuming calibration required, especially for continuum robots. For this reason, standard approaches for 3-D pose estimation involve the use of external tracking systems. Recently, image-based methods have emerged as promising, non-invasive alternatives. While many image-based approaches in the literature have shown accurate results, they generally require either a complex iterative optimization for each processed image, making them unsuitable for real-time applications, or a large number of manually-annotated images for efficient learning. In this paper we propose a self-supervised image-based method, exploiting, at training time only, the imprecise kinematic information provided by the robot. In order to avoid introducing time-consuming manual annotations, the problem is formulated as an auto-encoder, smartly bottlenecked by the presence of a physical model of the robotic instruments and surgical camera, forcing a separation between image background and kinematic content. Validation of the method was performed on semi-synthetic, phantom and in-vivo datasets, obtained using a flexible robotized endoscope, showing promising results for real-time image-based 3-D pose estimation of surgical instruments.

Related papers

• Realistic Data Generation for 6D Pose Estimation of Surgical Instruments [4.226502078427161]
  6D pose estimation of surgical instruments is critical to enable the automatic execution of surgical maneuvers. In household and industrial settings, synthetic data, generated with 3D computer graphics software, has been shown as an alternative to minimize annotation costs. We propose an improved simulation environment for surgical robotics that enables the automatic generation of large and diverse datasets.
  arXiv  Detail & Related papers  (2024-06-11T14:59:29Z)
• Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries [29.201385352740555]
  We propose a novel visual-kinematics graph learning framework to accurately segment the instrument tip given various surgical procedures. Specifically, a graph learning framework is proposed to encode relational features of instrument parts from both image and kinematics. A cross-modal contrastive loss is designed to incorporate robust geometric prior from kinematics to image for tip segmentation.
  arXiv  Detail & Related papers  (2023-09-02T14:52:58Z)
• Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose Estimation of Surgical Instruments [66.74633676595889]
  We present a multi-camera capture setup consisting of static and head-mounted cameras. Second, we publish a multi-view RGB-D video dataset of ex-vivo spine surgeries, captured in a surgical wet lab and a real operating theatre. Third, we evaluate three state-of-the-art single-view and multi-view methods for the task of 6DoF pose estimation of surgical instruments.
  arXiv  Detail & Related papers  (2023-05-05T13:42:19Z)
• Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer [26.21320177775571]
  We propose an end-to-end pose estimation framework that is capable of online camera-to-robot calibration and a self-supervised training method. Our framework combines deep learning and geometric vision for solving the robot pose, and the pipeline is fully differentiable.
  arXiv  Detail & Related papers  (2023-02-28T05:55:42Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• Context-Aware Sequence Alignment using 4D Skeletal Augmentation [67.05537307224525]
  Temporal alignment of fine-grained human actions in videos is important for numerous applications in computer vision, robotics, and mixed reality. We propose a novel context-aware self-supervised learning architecture to align sequences of actions. Specifically, CASA employs self-attention and cross-attention mechanisms to incorporate the spatial and temporal context of human actions.
  arXiv  Detail & Related papers  (2022-04-26T10:59:29Z)
• CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Data [11.92904350972493]
  Vision-based segmentation of the robotic tool during robot-assisted surgery enables downstream applications, such as augmented reality feedback. With the introduction of deep learning, many methods were presented to solve instrument segmentation directly and solely from images. We present CaRTS, a causality-driven robot tool segmentation algorithm, that is designed based on a complementary causal model of the robot tool segmentation task.
  arXiv  Detail & Related papers  (2022-03-15T22:26:19Z)
• Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery [10.562627972607892]
  We show that it may be possible to use robot kinematic data coupled with laparoscopic images to alleviate the labelling problem. We propose a new deep learning based model for parallel processing of both laparoscopic and simulation images.
  arXiv  Detail & Related papers  (2020-07-17T16:33:33Z)
• Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks [54.00217496410142]
  We propose an unpaired image-to-image translation where the goal is to learn the mapping between an input endoscopic image and a corresponding annotation. Our approach allows to train image segmentation models without the need to acquire expensive annotations. We test our proposed method on Endovis 2017 challenge dataset and show that it is competitive with supervised segmentation methods.
  arXiv  Detail & Related papers  (2020-07-09T01:39:39Z)
• Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery [58.63306322525082]
  Most applications rely on accurate real-time segmentation of high-resolution surgical images. We design a light-weight and highly-efficient deep residual architecture which is tuned to perform real-time inference of high-resolution images.
  arXiv  Detail & Related papers  (2020-07-08T21:38:29Z)
• Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction [118.21363599332493]
  We present a method to leverage photometric consistency across time when annotations are only available for a sparse subset of frames in a video. Our model is trained end-to-end on color images to jointly reconstruct hands and objects in 3D by inferring their poses. We achieve state-of-the-art results on 3D hand-object reconstruction benchmarks and demonstrate that our approach allows us to improve the pose estimation accuracy.
  arXiv  Detail & Related papers  (2020-04-28T12:03:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.