  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.
