2 research outputs found
Towards automatic initialization of registration algorithms using simulated endoscopy images
Registering images from different modalities is an active area of research in
computer aided medical interventions. Several registration algorithms have been
developed, many of which achieve high accuracy. However, these results are
dependent on many factors, including the quality of the extracted features or
segmentations being registered as well as the initial alignment. Although
several methods have been developed towards improving segmentation algorithms
and automating the segmentation process, few automatic initialization
algorithms have been explored. In many cases, the initial alignment from which
a registration is initiated is performed manually, which interferes with the
clinical workflow. Our aim is to use scene classification in endoscopic
procedures to achieve coarse alignment of the endoscope and a preoperative
image of the anatomy. In this paper, we show using simulated scenes that a
neural network can predict the region of anatomy (with respect to a
preoperative image) that the endoscope is located in by observing a single
endoscopic video frame. With limited training and without any hyperparameter
tuning, our method achieves an accuracy of 76.53 (+/-1.19)%. There are several
avenues for improvement, making this a promising direction of research. Code is
available at https://github.com/AyushiSinha/AutoInitialization.Comment: 4 pages, 4 figure
Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods
We present a self-supervised approach to training convolutional neural
networks for dense depth estimation from monocular endoscopy data without a
priori modeling of anatomy or shading. Our method only requires monocular
endoscopic videos and a multi-view stereo method, e.g., structure from motion,
to supervise learning in a sparse manner. Consequently, our method requires
neither manual labeling nor patient computed tomography (CT) scan in the
training and application phases. In a cross-patient experiment using CT scans
as groundtruth, the proposed method achieved submillimeter mean residual error.
In a comparison study to recent self-supervised depth estimation methods
designed for natural video on in vivo sinus endoscopy data, we demonstrate that
the proposed approach outperforms the previous methods by a large margin. The
source code for this work is publicly available online at
https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch.Comment: Accepted to IEEE Transactions on Medical Imagin