33 research outputs found
Image-Based Scene Analysis for Computer-Assisted Laparoscopic Surgery
This thesis is concerned on image-based scene analysis for computer-assisted laparoscopic surgery. The focus lies on how to extract different types of information from laparoscopic video data. Methods for semantic analysis can be used to determine what instruments and organs are currently visible and where they are located. Quantitative analysis provides numerical information on the size and distances of structures. Workflow analysis uses information from previously seen images to estimate the progression of surgery. To demonstrate that the proposed methods function in real-world scenarios, multiple evaluations on actual laparoscopic image data recorded from surgeries were performed. The proposed methods for semantic and quantitative analysis were successfully evaluated in live phantom and animal studies and also used during a live gastric bypass on a human patient
Non-rigid Point Cloud Registration for Middle Ear Diagnostics with Endoscopic Optical Coherence Tomography
Purpose: Middle ear infection is the most prevalent inflammatory disease,
especially among the pediatric population. Current diagnostic methods are
subjective and depend on visual cues from an otoscope, which is limited for
otologists to identify pathology. To address this shortcoming, endoscopic
optical coherence tomography (OCT) provides both morphological and functional
in-vivo measurements of the middle ear. However, due to the shadow of prior
structures, interpretation of OCT images is challenging and time-consuming. To
facilitate fast diagnosis and measurement, improvement in the readability of
OCT data is achieved by merging morphological knowledge from ex-vivo middle ear
models with OCT volumetric data, so that OCT applications can be further
promoted in daily clinical settings. Methods: We propose C2P-Net: a two-staged
non-rigid registration pipeline for complete to partial point clouds, which are
sampled from ex-vivo and in-vivo OCT models, respectively. To overcome the lack
of labeled training data, a fast and effective generation pipeline in Blender3D
is designed to simulate middle ear shapes and extract in-vivo noisy and partial
point clouds. Results: We evaluate the performance of C2P-Net through
experiments on both synthetic and real OCT datasets. The results demonstrate
that C2P-Net is generalized to unseen middle ear point clouds and capable of
handling realistic noise and incompleteness in synthetic and real OCT data.
Conclusion: In this work, we aim to enable diagnosis of middle ear structures
with the assistance of OCT images. We propose C2P-Net: a two-staged non-rigid
registration pipeline for point clouds to support the interpretation of in-vivo
noisy and partial OCT images for the first time. Code is available at:
https://gitlab.com/nct\_tso\_public/c2p-net
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
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