-
Automated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning
Purpose: We proposed a deep convolutional neural network (CNN), named Re...
read it
-
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images
Accurate isolation and quantification of intraocular dimensions in the a...
read it
-
Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning
Accurate and reproducible measurements of the aortic diameters are cruci...
read it
-
Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks
Scoliosis is a condition defined by an abnormal spinal curvature. For di...
read it
-
Deep Learning based Retinal OCT Segmentation
Our objective is to evaluate the efficacy of methods that use deep learn...
read it
-
Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks
Background: Three-dimensional (3D) cephalometric analysis using computer...
read it
-
BiofilmQuant: A Computer-Assisted Tool for Dental Biofilm Quantification
Dental biofilm is the deposition of microbial material over a tooth subs...
read it
Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks
Purpose: We developed a method to automatically locate and quantify graft detachment after Descemet's Membrane Endothelial Keratoplasty (DMEK) in Anterior Segment Optical Coherence Tomography (AS-OCT) scans. Methods: 1280 AS-OCT B-scans were annotated by a DMEK expert. Using the annotations, a deep learning pipeline was developed to localize scleral spur, center the AS-OCT B-scans and segment the detached graft sections. Detachment segmentation model performance was evaluated per B-scan by comparing (1) length of detachment and (2) horizontal projection of the detached sections with the expert annotations. Horizontal projections were used to construct graft detachment maps. All final evaluations were done on a test set that was set apart during training of the models. A second DMEK expert annotated the test set to determine inter-rater performance. Results: Mean scleral spur localization error was 0.155 mm, whereas the inter-rater difference was 0.090 mm. The estimated graft detachment lengths were in 69 the ground truth (77 horizontal projections of all B-scans with detachments were 0.896 and 0.880 for our model and the second DMEK expert respectively. Conclusion: Our deep learning model can be used to automatically and instantly localize graft detachment in AS-OCT B-scans. Horizontal detachment projections can be determined with the same accuracy as a human DMEK expert, allowing for the construction of accurate graft detachment maps. Translational Relevance: Automated localization and quantification of graft detachment can support DMEK research and standardize clinical decision making.
READ FULL TEXT
Comments
There are no comments yet.