基于深度学习的眼底图像视盘自动分割方法。

IF 2.3 Q3 MEDICAL INFORMATICS
Anindita Septiarini, Hamdani Hamdani, Emy Setyaningsih, Eko Junirianto, Fitri Utaminingrum
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引用次数: 1

摘要

目的:视盘是视网膜眼底图像结构的一部分,影响青光眼特征的提取。本研究提出了一种基于卷积神经网络(CNN)的深度学习自动分割视网膜眼底图像视盘区域的方法。方法:本研究使用了包含视网膜眼底图像的私人和公共数据集。私有数据集由350张图像组成,而公共数据集是视网膜眼底青光眼挑战(REFUGE)。该方法基于带有单镜头多盒检测器(MobileNetV2)的CNN,将原始图像调整为640 × 640的输入数据,形成感兴趣区域(ROI)图像。然后实现预处理序列,包括增强、调整大小和规范化。此外,采用U-Net模型对128 × 128输入数据进行视盘分割。结果:所提出的方法适用于所使用的数据集,private数据集的f1得分、dice得分和交集/并的值分别为0.9880、0.9852和0.9763,REFUGE数据集的f1得分、dice得分和交集/并的值分别为0.9854、0.9838和0.9712。结论:所提出的方法所产生的视盘面积与眼科医生鉴定的视盘面积相似。因此,可以考虑使用该方法实现视盘区域的自动分割。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images.

Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images.

Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images.

Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images.

Objectives: The optic disc is part of the retinal fundus image structure, which influences the extraction of glaucoma features. This study proposes a method that automatically segments the optic disc area in retinal fundus images using deep learning based on a convolutional neural network (CNN).

Methods: This study used private and public datasets containing retinal fundus images. The private dataset consisted of 350 images, while the public dataset was the Retinal Fundus Glaucoma Challenge (REFUGE). The proposed method was based on a CNN with a single-shot multibox detector (MobileNetV2) to form images of the region-of-interest (ROI) using the original image resized into 640 × 640 input data. A pre-processing sequence was then implemented, including augmentation, resizing, and normalization. Furthermore, a U-Net model was applied for optic disc segmentation with 128 × 128 input data.

Results: The proposed method was appropriately applied to the datasets used, as shown by the values of the F1-score, dice score, and intersection over union of 0.9880, 0.9852, and 0.9763 for the private dataset, respectively, and 0.9854, 0.9838 and 0.9712 for the REFUGE dataset.

Conclusions: The optic disc area produced by the proposed method was similar to that identified by an ophthalmologist. Therefore, this method can be considered for implementing automatic segmentation of the optic disc area.

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来源期刊
Healthcare Informatics Research
Healthcare Informatics Research MEDICAL INFORMATICS-
CiteScore
4.90
自引率
6.90%
发文量
44
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