Autonomous Tissue Scanning under Free-Form Motion for Intraoperative
Tissue Characterisation
- URL: http://arxiv.org/abs/2005.05050v3
- Date: Fri, 22 May 2020 12:37:53 GMT
- Title: Autonomous Tissue Scanning under Free-Form Motion for Intraoperative
Tissue Characterisation
- Authors: Jian Zhan, Joao Cartucho and Stamatia Giannarou
- Abstract summary: In Minimally Invasive Surgery (MIS), tissue scanning with imaging probes is required for subsurface visualisation.
We propose a visual servoing framework for autonomous tissue scanning, able to deal with free-form tissue deformation.
We deployed this framework on the da Vinci surgical robot using the da Vinci Research Kit (dVRK) for Ultrasound tissue scanning.
- Score: 3.5579740292581006
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In Minimally Invasive Surgery (MIS), tissue scanning with imaging probes is
required for subsurface visualisation to characterise the state of the tissue.
However, scanning of large tissue surfaces in the presence of deformation is a
challenging task for the surgeon. Recently, robot-assisted local tissue
scanning has been investigated for motion stabilisation of imaging probes to
facilitate the capturing of good quality images and reduce the surgeon's
cognitive load. Nonetheless, these approaches require the tissue surface to be
static or deform with periodic motion. To eliminate these assumptions, we
propose a visual servoing framework for autonomous tissue scanning, able to
deal with free-form tissue deformation. 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. A desired scanning trajectory is manually defined on a
reference frame and continuously updated using projective geometry to follow
the tissue motion and control the movement of the robotic arm. The advantage of
the proposed method is that it does not require the learning of the tissue
motion prior to scanning and can deal with free-form deformation. We deployed
this framework on the da Vinci surgical robot using the da Vinci Research Kit
(dVRK) for Ultrasound tissue scanning. Since the framework does not rely on
information from the Ultrasound data, it can be easily extended to other
probe-based imaging modalities.
Related papers
- Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted Surgery Using Structured Light [4.199824399433837]
This letter introduces an effective vision-based scheme that utilizes a One-Shot structured light projection with a designed pattern on soft tissue.
The images captured from the endoscopic stereo camera are analyzed to reconstruct high-resolution 3D point clouds for soft tissue deformation.
A modified PointNet-based force estimation method is proposed, which excels in representing the complex mechanical properties of soft tissue.
arXiv Detail & Related papers (2025-01-15T05:36:41Z) - Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation using Disentanglement Learning [5.354351782195383]
Motion artifacts are a significant challenge in Magnetic Resonance Imaging (MRI)
This study proposes a novel deep learning framework that demonstrates superior performance in both motion correction and robust brain tissue segmentation.
In-vivo experiments on pediatric motion data demonstrate that our proposed framework outperforms state-of-the-art methods in segmenting motion-corrupted MRI scans.
arXiv Detail & Related papers (2024-12-05T06:52:42Z) - FLex: Joint Pose and Dynamic Radiance Fields Optimization for Stereo Endoscopic Videos [79.50191812646125]
Reconstruction of endoscopic scenes is an important asset for various medical applications, from post-surgery analysis to educational training.
We adress the challenging setup of a moving endoscope within a highly dynamic environment of deforming tissue.
We propose an implicit scene separation into multiple overlapping 4D neural radiance fields (NeRFs) and a progressive optimization scheme jointly optimizing for reconstruction and camera poses from scratch.
This improves the ease-of-use and allows to scale reconstruction capabilities in time to process surgical videos of 5,000 frames and more; an improvement of more than ten times compared to the state of the art while being agnostic to external tracking information
arXiv Detail & Related papers (2024-03-18T19:13:02Z) - AiAReSeg: Catheter Detection and Segmentation in Interventional
Ultrasound using Transformers [75.20925220246689]
endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature.
This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
arXiv Detail & Related papers (2023-09-25T19:34:12Z) - Identifying Visible Tissue in Intraoperative Ultrasound Images during
Brain Surgery: A Method and Application [1.4408275800058263]
Intraoperative ultrasound scanning is a demanding visuotactile task.
It requires operators to simultaneously localise the ultrasound perspective and manually perform slight adjustments to the pose of the probe.
We propose a method for the identification of the visible tissue, which enables the analysis of ultrasound probe and tissue contact.
arXiv Detail & Related papers (2023-06-01T23:06:14Z) - Neural LerPlane Representations for Fast 4D Reconstruction of Deformable
Tissues [52.886545681833596]
LerPlane is a novel method for fast and accurate reconstruction of surgical scenes under a single-viewpoint setting.
LerPlane treats surgical procedures as 4D volumes and factorizes them into explicit 2D planes of static and dynamic fields.
LerPlane shares static fields, significantly reducing the workload of dynamic tissue modeling.
arXiv Detail & Related papers (2023-05-31T14:38:35Z) - Collaborative Robotic Ultrasound Tissue Scanning for Surgical Resection
Guidance in Neurosurgery [1.372026330898297]
The aim of this paper is to introduce a robotic platform for autonomous iUS tissue scanning.
A key application of the proposed platform is the scanning of brain tissue to guide tumour resection.
arXiv Detail & Related papers (2023-01-19T17:05:07Z) - Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of
Articulated Motion [48.52403516006036]
This paper proposes a vision-based approach allowing autonomous robotic US limb scanning.
To this end, an atlas MRI template of a human arm with annotated vascular structures is used to generate trajectories.
In all cases, the system can successfully acquire the planned vascular structure on volunteers' limbs.
arXiv Detail & Related papers (2022-08-10T15:39:20Z) - Robust Landmark-based Stent Tracking in X-ray Fluoroscopy [10.917460255497227]
We propose an end-to-end deep learning framework for single stent tracking.
It consists of three hierarchical modules: U-Net based landmark detection, ResNet based stent proposal and feature extraction.
Experiments show that our method performs significantly better in detection compared with the state-of-the-art point-based tracking models.
arXiv Detail & Related papers (2022-07-20T14:20:03Z) - Deep Learning for Ultrasound Beamforming [120.12255978513912]
Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, lies at the heart of the ultrasound image formation chain.
Modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing.
Deep learning methods can play a compelling role in the digital beamforming pipeline.
arXiv Detail & Related papers (2021-09-23T15:15:21Z) - Tattoo tomography: Freehand 3D photoacoustic image reconstruction with
an optical pattern [49.240017254888336]
Photoacoustic tomography (PAT) is a novel imaging technique that can resolve both morphological and functional tissue properties.
A current drawback is the limited field-of-view provided by the conventionally applied 2D probes.
We present a novel approach to 3D reconstruction of PAT data that does not require an external tracking system.
arXiv Detail & Related papers (2020-11-10T09:27:56Z)
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.