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Automated stereo vision instrument tracking for intraoperative OCT guided anterior segment ophthalmic surgical maneuvers.

ABSTRACT: Microscope-integrated intraoperative OCT (iOCT) enables imaging of tissue cross-sections concurrent with ophthalmic surgical maneuvers. However, limited acquisition rates and complex three-dimensional visualization methods preclude real-time surgical guidance using iOCT. We present an automated stereo vision surgical instrument tracking system integrated with a prototype iOCT system. We demonstrate, for the first time, automatically tracked video-rate cross-sectional iOCT imaging of instrument-tissue interactions during ophthalmic surgical maneuvers. The iOCT scan-field is automatically centered on the surgical instrument tip, ensuring continuous visualization of instrument positions relative to the underlying tissue over a 2500 mm(2) field with sub-millimeter positional resolution and <1° angular resolution. Automated instrument tracking has the added advantage of providing feedback on surgical dynamics during precision tissue manipulations because it makes it possible to use only two cross-sectional iOCT images, aligned parallel and perpendicular to the surgical instrument, which also reduces both system complexity and data throughput requirements. Our current implementation is suitable for anterior segment surgery. Further system modifications are proposed for applications in posterior segment surgery. Finally, the instrument tracking system described is modular and system agnostic, making it compatible with different commercial and research OCT and surgical microscopy systems and surgical instrumentations. These advances address critical barriers to the development of iOCT-guided surgical maneuvers and may also be translatable to applications in microsurgery outside of ophthalmology.

SUBMITTER: El-Haddad MT

PROVIDER: S-EPMC4541528 | biostudies-other | 2015 Aug

REPOSITORIES: biostudies-other

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Project description:PURPOSE:To report the 3-year assessment of feasibility and usefulness of microscope-integrated intraoperative OCT (iOCT) during ophthalmic surgery. DESIGN:Prospective, consecutive case series. PARTICIPANTS:Adult participants undergoing incisional ophthalmic surgery with iOCT imaging who consented to be enrolled in the Determination of Feasibility of Intraoperative Spectral-Domain Microscope Combined/Integrated OCT Visualization during En Face Retinal and Ophthalmic Surgery (DISCOVER) study. METHODS:The DISCOVER study is a single-site, multisurgeon, institutional review board-approved investigational device prospective study. Participants included patients undergoing anterior or posterior segment surgery who underwent iOCT imaging with 1 of 3 prototype microscope-integrated iOCT systems (i.e., Zeiss Rescan 700, Leica EnFocus, or Cole Eye iOCT systems). Clinical characteristics were documented, iOCT was directed by the operating surgeon at predetermined surgical time points, and each surgeon completed a questionnaire after surgery to evaluate the usefulness of iOCT during surgery. MAIN OUTCOME MEASURES:Feasibility of iOCT based ability to obtain an OCT image during surgery and usefulness of iOCT based on surgeon reporting during surgery. RESULTS:Eight hundred thirty-seven eyes (244 anterior segment cases and 593 posterior segment cases) were enrolled in the DISCOVER study. Intraoperative OCT demonstrated feasibility with successful image acquisition in 820 eyes (98.0%; 95% confidence interval [CI], 96.8%-98.8%). In 106 anterior segment cases (43.4%; 95% CI, 37.1%-49.9%), the surgeons indicated that the iOCT information impacted their surgical decision making and altered the procedure. In posterior segment procedures, surgeons reported that iOCT enabled altered surgical decision making during the procedure in 173 cases (29.2%; 95% CI, 25.5%-33.0%). CONCLUSIONS:The DISCOVER iOCT study demonstrated both generalized feasibility and usefulness based on the surgeon-reported impact on surgical decision making. This large-scale study confirmed similar findings from other studies on the potential value and impact of iOCT on ophthalmic surgery.

Project description:PURPOSE:To develop and test deep learning classifiers that detect gonioscopic angle closure and primary angle closure disease (PACD) based on fully automated analysis of anterior segment OCT (AS-OCT) images. METHODS:Subjects were recruited as part of the Chinese-American Eye Study (CHES), a population-based study of Chinese Americans in Los Angeles, California, USA. Each subject underwent a complete ocular examination including gonioscopy and AS-OCT imaging in each quadrant of the anterior chamber angle (ACA). Deep learning methods were used to develop 3 competing multi-class convolutional neural network (CNN) classifiers for modified Shaffer grades 0, 1, 2, 3, and 4. Binary probabilities for closed (grades 0 and 1) and open (grades 2, 3, and 4) angles were calculated by summing over the corresponding grades. Classifier performance was evaluated by 5-fold cross-validation and on an independent test dataset. Outcome measures included area under the receiver operating characteristic curve (AUC) for detecting gonioscopic angle closure and PACD, defined as either 2 or 3 quadrants of gonioscopic angle closure per eye. RESULTS:A total of 4036 AS-OCT images with corresponding gonioscopy grades (1943 open, 2093 closed) were obtained from 791 CHES subjects. Three competing CNN classifiers were developed with a cross-validation dataset of 3396 images (1632 open, 1764 closed) from 664 subjects. The remaining 640 images (311 open, 329 closed) from 127 subjects were segregated into a test dataset. The best-performing classifier was developed by applying transfer learning to the ResNet-18 architecture. For detecting gonioscopic angle closure, this classifier achieved an AUC of 0.933 (95% confidence interval, 0.925-0.941) on the cross-validation dataset and 0.928 on the test dataset. For detecting PACD based on 2- and 3-quadrant definitions, the ResNet-18 classifier achieved AUCs of 0.964 and 0.952, respectively, on the test dataset. CONCLUSION:Deep learning classifiers effectively detect gonioscopic angle closure and PACD based on automated analysis of AS-OCT images. These methods could be used to automate clinical evaluations of the ACA and improve access to eye care in high-risk populations.

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Automated stereo vision instrument tracking for intraoperative OCT guided anterior segment ophthalmic surgical maneuvers.

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