Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study.

PURPOSE: To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source optical coherence tomography (SS-OCT) and OCT-angiography (OCT-A) data. METHODS: In this phase IV, retrospective, proof of concept, single center study, SS-OCT data from 17 previously treated nARMD eyes was used to assess retinal layer thicknesses, as well as quantify intraretinal fluid (IRF), subretinal fluid (SRF), and serous pigment epithelium detachments (PEDs) using a novel deep learning-based, macular fluid segmentation algorithm. Baseline OCT and OCT-A morphological features and fluid measurements were correlated using the Pearson correlation coefficient (PCC) to changes in BCVA from baseline to week 52. RESULTS: Total retinal fluid (IRF, SRF and PED) volume at baseline had the strongest correlation to improvement in BCVA at month 12 (PCC = 0.652, p = 0.005). Fluid was subsequently sub-categorized into IRF, SRF and PED, with PED volume having the next highest correlation (PCC = 0.648, p = 0.005) to BCVA improvement. Average total retinal thickness in isolation demonstrated poor correlation (PCC = 0.334, p = 0.189). When two features, mean choroidal neovascular membranes (CNVM) size and total fluid volume, were combined and correlated with visual outcomes, the highest correlation increased to PCC = 0.695 (p = 0.002). CONCLUSIONS: In isolation, total fluid volume most closely correlates with change in BCVA values between baseline and week 52. In combination with complimentary information from OCT-A, an improvement in the linear correlation score was observed. Average total retinal thickness provided a lower correlation, and thus provides a lower predictive outcome than alternative metrics assessed. Clinically, a machine-learning approach to analyzing fluid metrics in combination with lesion size may provide an advantage in personalizing therapy and predicting BCVA outcomes at week 52

A unified graphical models framework for automated human embryo tracking in time lapse microscopy

Time lapse microscopy has emerged as an important modality for studying early human embryo development. Detection of certain events can provide insight into embryo health and fate. Embryo tracking is challenged by a high dimensional search space, weak features, outliers, occlusions, missing data, multiple interacting deformable targets, changing topology, and a weak motion model. We address these with a data driven approach that uses a rich set of discriminative image and geometric features and their spatiotemporal context. We pose the mitosis detection problem as augmented simultaneous segmentation and classification in a conditional random field framework that combines tracking based and tracking free elements. For 275 clinical image sequences we measured division events during the first 48 hours of embryo development to within 30 minutes resulting in an improvement of 24.2% over a tracking-based approach and a 35.7% improvement over a tracking-free approach, and more than an order of magnitude improvement over a traditional particle filter, demonstrating the success of our framework

A Unified Graphical Models Framework for Automated Mitosis Detection in Human Embryos

Abstract—Time lapse microscopy has emerged as an important modality for studying human embryo development, as mitosis events can provide insight into embryo health and fate. Mi-tosis detection can happen through tracking of embryonic cells (tracking based), or from low level image features and classifiers (tracking free). Tracking based approaches are challenged by high dimensional search space, weak features, outliers, missing data, multiple deformable targets, and weak motion model. Tracking free approaches are data driven and complement tracking based approaches. We pose mitosis detection as augmented simultaneous segmentation and classification in a conditional random field (CRF) framework that combines both approaches. It uses a rich set of discriminative features and their spatiotemporal context. It performs a dual pass approximate inference that addresses the high dimensionality of tracking and combines results from both components. For 312 clinical sequences we measured divi-sion events to within 30 min and observed an improvement of 25.6 % and a 32.9 % improvement over purely tracking based and tracking free approach respectively, and close to an order of magnitude over a traditional particle filter. While our work was motivated by human embryo development, it can be extended to other detection problems in image sequences of evolving cell populations. Index Terms—Data driven Monte Carlo, embryo tracking, graphical models, mitosis detection. I

Markerless Real-Time Target Region Tracking: Application to Frameless Stereotactic Radiosurgery

Accurate and fast registration of intra-operative 2D projection images to 3D pre-operative images is an important component of many image-guided surgical procedures. If the 2D image acquisition is repeated several times during the procedure, the registration problem can be cast instead as a 3D tracking problem. To solve the 3D problem, we propose in this paper to apply a real-time 2D region tracking algorithm to first recover the components of the transformation that are in-plane to the projections. From the 2D motion estimates of all projections, a consistent estimate of the 3D motion is derived. We compare this method to computation in 3D and a combination of both. Using clinical data with a goldstandard transformation, we show that a standard tracking algorithm is capable of accurately and robustly tracking regions in x-ray projection images, and that the use of 2D tracking greatly improves the accuracy and speed of 3D tracking