# Extracting Retinal Anatomy and Pathological Structure Using Multiscale Segmentation
###### Abstract  
Fundus image segmentation technology has always been an important tool in the medical imaging field. Recent studies have validated that deep learning techniques can effectively segment retinal anatomy and determine pathological structure in retinal fundus photographs. However, several groups of image segmentation methods used in medical imaging only provide a single retinopathic feature (e.g., roth spots and exudates). In this paper, we propose a more accurate and clinically oriented framework for the segmentation of fundus images from end-to-end input. We design a four-path multiscale input network structure that learns network features and finds overall characteristics via our network. Our network's structure is not limited by segmentation of single retinopathic features. Our method is suitable for exudates, roth spots, blood vessels, and optic discs segmentation. The structure has general applicability to many fundus models; therefore, we use our own dataset for training. In cooperation with hospitals and board-certified ophthalmologists, the proposed framework is validated on retinal images from large databases and can improve diagnostic performance compared to state-of-the-art methods that use smaller databases for training. The proposed framework detects blood vessels with an accuracy of 0.927, which is comparable to exudate accuracy (0.939) and roth spot accuracy (0.904), providing ophthalmologists with a practical diagnostic and a robust analytical tool. Keywords:retinopathy feature; deep learning; classified database; image processing; multiscale segmentation +
Footnote †: journal: Applied  
segmentation methods in medical imaging includes superpixel segmentation methods [7; 8], watershed segmentation methods [9; 10], and active contour methods [11; 12; 13]. In 2016, [PERSON] et al. [7] proposed a superpixel-based 3D graph-cutting algorithm that segments prostate surfaces. [PERSON] et al. [8] propose a multi-atlas fusion framework to automatically segment pelvic structure from 45 to 135-degree oblique X-ray radiographic images. In 2004, [PERSON], [PERSON] et al. [9] integrate the advantages of neural network (NN) classification and morphological watershed segmentation to extract precise contours of breast tumors from US images. [PERSON] et al. [10] present a new automatic system for liver segmentation from abdominal MRI images. [PERSON] et al. [11; 12] analyzed comparative research to assess the utility and performance of three active contour methods (ACMs) for segmenting the corpus callosum (CC) from magnetic resonance (MR) images of the brain. [PERSON] et al. [13] propose a new infinite active contour model that uses hybrid region information to approach segmentation of blood vessel. In recent years, deep learning algorithms have made key breakthroughs by finding shallow features from abstract deep features. Compared with traditional methods, deep learning uses a computer program to autonomously learn from large sets of imaging data. These methods often solve problems that can be solved by traditional algorithms at lower computational cost and with better efficiency. Many segmentation methods for fundus images have been widely reported. For example, [PERSON] et al. [14] simplified a convolutional neural network (CNN) structure to distinguish between large and small blood vessels (and background) in fundal images. These methods further adjust images of different sizes via convolution kernels and can often obtain good segmentation results using a digital retinal image blood vessel extraction library [15]. [PERSON] et al. [16] proposed a retinal image vessel segmentation method based on CNNs and a conditional random field (CRF). This method treats the blood vessel segmentation step as a boundary detection problem and uses a CNN to generate a segmentation probability map. Finally, these methods combine CRFs to obtain a binary diagnostic result. AlexNet [17] and VGGNet [18] methods were proposed for image segmentation next, but there are still many drawbacks.