Abstract: Some embodiments of the disclosure provide a method for measuring lesion features of hypertensive retinopathy. In some examples, the method includes: acquiring a fundus image (s110); identifying an optic disc region of the fundus image and dividing the fundus image into at least three regions including a first region (c1), a second region (c2), and a third region (c3) on the basis of the optic disc region (b) (s120); performing artery and vein segmentation on the fundus image by a deep learning-based arteriovenous segmentation model to obtain the arteriovenous segmentation result (s130), the arteriovenous vessel annotation results including an artery annotation result (e1), a vein annotation result (e2), and a small vessel annotation result (e3); and measuring lesion features in the fundus image on the basis of the three regions (c1, c2, c3) and the arteriovenous segmentation result (s140).

Abstract: Some embodiments of the disclosure provide a quality control method for data annotation on a fundus image. In some examples, the method includes: acquiring a plurality of fundus images; performing standardization processing on the fundus images to obtain a plurality of standardized fundus images; performing preliminary filtering on quality of the standardized fundus images to acquire a plurality of qualified fundus images; preparing a target fundus image set; a plurality of first annotation doctors respectively annotating the images of the target fundus image set, to acquire a plurality of groups of doctor annotation results; calculating, on the basis of the doctor annotation results, self-consistency and gold-standard consistency of the corresponding first annotation doctors, to acquire the doctor annotation results of the first annotation doctors satisfying a preset condition as target annotation results; and gathering a plurality of groups of target annotation results to acquire a final annotation result.

Abstract: Some embodiments of the disclosure provide a measuring method and measuring apparatus of a blood vessel diameter of a fundus image. In some examples, the measuring method include the following steps: acquiring a fundus image, generating a blood vessel segmentation image based on the fundus image, performing resolution enhancement on the blood vessel segmentation image, extracting blood vessel skeletons from the enhanced blood vessel segmentation image and performing fitting on the blood vessel skeletons to obtain a continuous blood vessel skeleton and a vessel diameter measurement direction of measurement pixel points, generating a blood vessel contour corresponding to the measurement pixel points based on the enhanced blood vessel segmentation image, and calculating a blood vessel diameter corresponding to the measurement pixel points based on a number of blood vessel pixel points in the blood vessel contour.

Abstract: Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: The present disclosure provides an artificial neural network system for identifying a lesion in a retinal fundus image that comprises a pre-processing module configured to separately pre-process a target retinal fundus image and a reference retinal fundus image taken from a same person; a first neural network (12) configured to generate a first advanced feature set from the target retinal fundus image; a second neural network (22) configured to generate a second advanced feature set from the reference retinal fundus image; a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set; and a third neural network (14) configured to generate, according to the feature combination set, a diagnosis result.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.