Generating corneal panoramic images from contact specular microscope images
The contact specular microscope has a wider angle of view than that of the non-contact specular microscope but still cannot capture an image of the entire cornea. To obtain such an image, it is necessary to prepare film on the parts of the image captured sequentially and combine them to create a complete image. This study proposes a framework to automatically generate an entire corneal image from videos captured using a contact specular microscope. Relatively focused images were extracted from the videos and panoramic compositing was performed. If an entire image can be generated, it is possible to detect guttae from the image and examine the extent of their presence. The system was implemented and the effectiveness of the proposed framework was examined. The system was implemented using custom-made composite software, Image Composite Software (ICS, K.I. Technology Co., Ltd., Japan, internal algorithms not disclosed), and a supervised learning model using U-Net was used for guttae detection. Several images were correctly synthesized when the constructed system was applied to 94 different corneal videos obtained from Fuchs endothelial corneal dystrophy (FECD) mouse model. The implementation and application of the method to the data in this study confirmed its effectiveness. Owing to the minimal quantitative evaluation performed, such as accuracy with implementation, it may pose some limitations for future investigations.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae
To estimate the corneal endothelial parameters from specular microscopy images depicting cornea guttata (Fuchs dystrophy), we propose a new deep learning methodology that includes a novel attention mechanism named feedba…
Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights
Sophisticated generative adversary network (GAN) models are now able to synthesize highly realistic human faces that are difficult to discern from real ones visually. In this work, we show that GAN synthesized faces can …
Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps
Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs' dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that…
regressionSegmentationAccurate Tissue Interface Segmentation via Adversarial Pre-Segmentation of Anterior Segment OCT Images
Optical Coherence Tomography (OCT) is an imaging modality that has been widely adopted for visualizing corneal, retinal and limbal tissue structure with micron resolution. It can be used to diagnose pathological conditio…
Generative Adversarial NetworkSegmentationExploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image
Corneal diseases are the most common eye disorders. Deep learning techniques are used to per-form automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakn…
Deep LearningGenerative Adversarial NetworkMedical Diagnosis