Despite a strong evidence of the clinical and economic benefits of minimally invasive surgery (MIS) for many common surgical procedures, there is a gross underutilization of MIS in many US hospitals, potentially due to its steep learning curve. Intraoperative videos captured using a camera inserted into the body during MIS procedures are emerging as an invaluable resource for MIS education, skill assessment and quality assurance. However, these videos often have a duration of several hours and there is a pressing need for automated tools to help surgeons quickly find key semantic segments of interest within MIS videos. In this paper, we present a novel integrated approach for facilitating content-based retrieval of video segments that are semantically similar to a query video within a large collection of MIS videos. We use state-of-theart deep 3D convolutional neural network (CNN) models pre-trained on large public video classification datasets to extract spatiotemporal features from MIS video segments and employ an iterative query refinement (IQR) strategy where in a support vector machine (SVM) classifier trained online based on relevance feedback from the user is used to refine the search results iteratively. We show that our method outperforms the state-of-the-art on the SurgicalActions160 dataset containing 160 video clips of typical surgical actions in gynecologic MIS procedures.
Studies show that cracked teeth are the third most common cause for tooth loss in industrialized countries. If detected early and accurately, patients can retain their teeth for a longer time. Most cracks are not detected early because of the discontinuous symptoms and lack of good diagnostic tools. Currently used imaging modalities like Cone Beam Computed Tomography (CBCT) and intraoral radiography often have low sensitivity and do not show cracks clearly. This paper introduces a novel method that can detect, quantify, and localize cracks automatically in high resolution CBCT (hr-CBCT) scans of teeth using steerable wavelets and learning methods. These initial results were created using hr-CBCT scans of a set of healthy teeth and of teeth with simulated longitudinal cracks. The cracks were simulated using multiple orientations. The crack detection was trained on the most significant wavelet coefficients at each scale using a bagged classifier of Support Vector Machines. Our results show high discriminative specificity and sensitivity of this method. The framework aims to be automatic, reproducible, and open-source. Future work will focus on the clinical validation of the proposed techniques on different types of cracks ex-vivo. We believe that this work will ultimately lead to improved tracking and detection of cracks allowing for longer lasting healthy teeth.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.