1,752 research outputs found
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
Objective: Surgical data science is evolving into a research field that aims
to observe everything occurring within and around the treatment process to
provide situation-aware data-driven assistance. In the context of endoscopic
video analysis, the accurate classification of organs in the field of view of
the camera proffers a technical challenge. Herein, we propose a new approach to
anatomical structure classification and image tagging that features an
intrinsic measure of confidence to estimate its own performance with high
reliability and which can be applied to both RGB and multispectral imaging (MI)
data. Methods: Organ recognition is performed using a superpixel classification
strategy based on textural and reflectance information. Classification
confidence is estimated by analyzing the dispersion of class probabilities.
Assessment of the proposed technology is performed through a comprehensive in
vivo study with seven pigs. Results: When applied to image tagging, mean
accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB)
and 96% (MI) with the confidence measure. Conclusion: Results showed that the
confidence measure had a significant influence on the classification accuracy,
and MI data are better suited for anatomical structure labeling than RGB data.
Significance: This work significantly enhances the state of art in automatic
labeling of endoscopic videos by introducing the use of the confidence metric,
and by being the first study to use MI data for in vivo laparoscopic tissue
classification. The data of our experiments will be released as the first in
vivo MI dataset upon publication of this paper.Comment: 7 pages, 6 images, 2 table
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
Concurrent Segmentation and Localization for Tracking of Surgical Instruments
Real-time instrument tracking is a crucial requirement for various
computer-assisted interventions. In order to overcome problems such as specular
reflections and motion blur, we propose a novel method that takes advantage of
the interdependency between localization and segmentation of the surgical tool.
In particular, we reformulate the 2D instrument pose estimation as heatmap
regression and thereby enable a concurrent, robust and near real-time
regression of both tasks via deep learning. As demonstrated by our experimental
results, this modeling leads to a significantly improved performance than
directly regressing the tool position and allows our method to outperform the
state of the art on a Retinal Microsurgery benchmark and the MICCAI EndoVis
Challenge 2015.Comment: I. Laina and N. Rieke contributed equally to this work. Accepted to
MICCAI 201
ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools
Real-time tool segmentation from endoscopic videos is an essential part of
many computer-assisted robotic surgical systems and of critical importance in
robotic surgical data science. We propose two novel deep learning architectures
for automatic segmentation of non-rigid surgical instruments. Both methods take
advantage of automated deep-learning-based multi-scale feature extraction while
trying to maintain an accurate segmentation quality at all resolutions. The two
proposed methods encode the multi-scale constraint inside the network
architecture. The first proposed architecture enforces it by cascaded
aggregation of predictions and the second proposed network does it by means of
a holistically-nested architecture where the loss at each scale is taken into
account for the optimization process. As the proposed methods are for real-time
semantic labeling, both present a reduced number of parameters. We propose the
use of parametric rectified linear units for semantic labeling in these small
architectures to increase the regularization ability of the design and maintain
the segmentation accuracy without overfitting the training sets. We compare the
proposed architectures against state-of-the-art fully convolutional networks.
We validate our methods using existing benchmark datasets, including ex vivo
cases with phantom tissue and different robotic surgical instruments present in
the scene. Our results show a statistically significant improved Dice
Similarity Coefficient over previous instrument segmentation methods. We
analyze our design choices and discuss the key drivers for improving accuracy.Comment: Paper accepted at IROS 201
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
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