Slit-lamp microscope, ophthalmic information processing device, ophthalmic system, slit-lamp microscope control method, program, and recording medium

Abstract

A slit-lamp microscope according to an exemplary embodiment includes a scanning unit and a data processing unit. The scanning unit collects a plurality of cross-sectional images by scanning an anterior eye portion of an eye to be examined with slit light. The data processing unit generates opacification distribution information indicating the distribution of opacification portions of the lens of the eye to be examined on the basis of the plurality of cross-sectional images collected by the scanning unit.

Description

technical field [0001] The invention relates to a slit lamp microscope, an ophthalmic information processing device, an ophthalmic system, a control method, a program and a recording medium of the slit lamp microscope. Background technique [0002] In the field of ophthalmology, image diagnosis plays an important role. In image diagnosis, various ophthalmic imaging devices are used. Types of ophthalmic imaging devices include a slit lamp microscope, a fundus camera, a scanning laser ophthalmoscope (SLO), an optical coherence tomography (OCT), and the like. [0003] One of the most widely and frequently used of such various ophthalmic devices is the slit lamp microscope. The slit lamp microscope is used to illuminate the subject's eye with slit light, and observe or photograph the illuminated cross-section with a microscope from oblique or sideways (for example, refer to Patent Documents 1 to 3). [0004] One of the main uses of the slit lamp microscope is to observe the a...

AI Technical Summary

Problems solved by technology

[0006] First, there is a problem that since the reflected light from the retina is used, it is difficult to manage the brightness of the illuminated image, and it is also difficult to manage (control, adjust) the image quality, so it is not suitable for quantitative diagnosis

Therefore, the diagnosis using the penetrating method largely depends on the subjectivity of the image reader, for example, the grade of cataract cannot be evaluated objectively.

In addition, although automatic image analysis using analysis programs and machine learning has developed rapidly in recent years, the difficulty of image quality management is one of the factors that prevent automatic image analysis from being applied to through-illuminated images

[0007] Second, the problem is that the through-illumination image is a planar image (projection image, projective image) with the fundus as the secondary light source, and there is no information in the depth direction (depth direction, Z direction), so it is impossible to grasp the three-dimensional turbidity distribution

That is to say, the illuminated image can only provide the turbidity distribution on the XY plane orthogonal to the Z direction, but not the turbidity distribution in the Z direction

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