10,739 research outputs found
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. The problem is challenging because different illumination conditions, bleeding, smoke and occlusions can reduce algorithm robustness. At present labelled data for training deep learning models is still lacking for semantic surgical instrument segmentation and in this paper 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 for robust segmentation of surgical tools. Due to the lack of laparoscopic frames annotated with both segmentation ground truth and kinematic information a new custom dataset was generated using the da Vinci Research Kit (dVRK) and is made available
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. The problem is challenging because different
illumination conditions, bleeding, smoke and occlusions can reduce algorithm
robustness. At present labelled data for training deep learning models is still
lacking for semantic surgical instrument segmentation and in this paper 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 for
robust segmentation of surgical tools. Due to the lack of laparoscopic frames
annotated with both segmentation ground truth and kinematic information a new
custom dataset was generated using the da Vinci Research Kit (dVRK) and is made
available
Unsupervised Learning of Surgical Smoke Removal from Simulation
The surgical smoke produced during minimally invasive surgery can not only reduce the visibility of the surgeons, but also severally affect the performance of image processing algorithms used for image guided surgery such as image tracking, segmentation, detection and retrieval. Besides from physical smoke evacuation devices, many research works address this issue by using vision-based methods to filter out the smoke and try to recover the clear images. More recently, end-to-end deep learning approaches have been introduced to solve the de-hazing and de-smoking problems. However, it is extremely difficult to collect large amounts of data for the effective learning of the implicit de-smoking function, especially for surgical scenes. In this paper, we propose a computational framework for unsupervised learning of smoke removal from rendering smoke on laparoscopic video. Compared to conventional image processing approaches, our proposed framework is able to remove local smoke and recover more realistic tissue colour but will not affect the areas without smoke. Although trained on synthetic images, the experimental results show that our network is able to effectively remove smoke on laparoscopic images with real surgical smoke
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
Intraoperative segmentation and tracking of minimally invasive instruments is
a prerequisite for computer- and robotic-assisted surgery. Since additional
hardware like tracking systems or the robot encoders are cumbersome and lack
accuracy, surgical vision is evolving as promising techniques to segment and
track the instruments using only the endoscopic images. However, what is
missing so far are common image data sets for consistent evaluation and
benchmarking of algorithms against each other. 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. The contribution of the paper is twofold: we introduce a comprehensive
validation data set that was provided to the study participants and present the
results of the comparative validation study. Based on the results of the
validation study, we arrive at the conclusion that modern deep learning
approaches outperform other methods in instrument segmentation tasks, but the
results are still not perfect. Furthermore, we show that merging results from
different methods actually significantly increases accuracy in comparison to
the best stand-alone method. On the other hand, the results of the instrument
tracking task show that this is still an open challenge, especially during
challenging scenarios in conventional laparoscopic surgery
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote sensing. Deep learning methods have shown impressive results and are
now the new state of the art for a wide range of computer vision tasks
including image and video recognition and segmentation. In particular,
Convolutional Neural Networks (CNNs) have recently proven to be well suited for
texture analysis with a design similar to a filter bank approach. In this
paper, we develop a new approach to DT analysis based on a CNN method applied
on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames
and temporal slices extracted from the DT sequences and combine their outputs
to obtain a competitive DT classifier. Our results on a wide range of commonly
used DT classification benchmark datasets prove the robustness of our approach.
Significant improvement of the state of the art is shown on the larger
datasets.Comment: 19 pages, 10 figure
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. 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
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