# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 1 Introduction  
The number of people diagnosed with diabetes has been increasing at an alarming rate during the past two decades. This accounts for almost half a billion people in the world as per the IDF Diabetes Atlas [1], which makes it a global health concern. This includes people of all ages. By 2045, this figure is supposed to reach seven hundred million and by the year 2040, one in three persons with this ailment will have DR, which is an ailment characterized by the occurrence of damaged blood vessels in the back of the retina. If it is not detected in a timely manner, it can lead to serious problems, like loss of sight. This makes DR a topic of the utmost importance. Doctors must physically analyze fundus images to determine the presence of this disease and to grade it. Fundus images offer graphical records that detail the current ophthalmic form of an individual's retina. This is a very time-consuming process, and in many countries the number of available trained professionals may not be proportionate to the number of patients. This may hinder providing timely treatment to many patients. Diabetes patients are instructed by doctors to have a routine medical screening of their fundus. Still many DR cases are left unnoticed until they reach a critical state. Due to this reason, it is advantageous to have computerized systems that assist in the diagnosis of DR. Researchers are developing automated systems for DR diagnosis by identifying the existence of certain DR lesions in fundus images. Few of the DR lesions include MA, HM, EX consisting of soft exudates (SE) and hard exudates(HE). DR grades are also established with the help of lesions present in the fundus. For example, the preliminary stage of DR is marked by the beginning of the growth of new blood vessels whose walls weaken, and tiny bulges or MA protrude from them. Later, when DR progresses to proliferative DR, other lesions like HE and SE may develop. HE are little white/yellowish-white deposits with crisp boundaries whereas, SE/Cotton-wood spots are light yellow or white areas which have unclear edges. Additionally, abnormal blood vessels may grow and bleed causing HM. Hence, some researchers are focusing on the detection and segmentation of DR lesions likeMA, HM, and EX which will aid in developing better DR diagnoses/grading for automated systems. For example, Figure 1 shows each of these DR lesions, whereas Figure 2 provides a visualization of a healthy retina vs an unhealthy retina. There are many challenges to developing such automated systems. The size of these lesions vary in a temporal manner and the OD, and fovea may be mistaken for lesions. Due to this reason, some researchers are focusing on OD/fovea detection which can also help to improve such systems. Another category of studies that are related to DR include retinal blood vessel segmentation studies. These are important to detect several medical disorders like DR and hypertensive retinopathy. Atypical variations in the retinal vessel will give an idea of the severity of several non-ophthalmic ailments like DR, diabetes mellitus, hypertension, arteriosclerosis, cardiovascular disease, and stroke [4]. Hence, it is important for diagnosing these diseases, especially DR.  
Several DL methods were used by researchers to accomplish these types of DR studies. We present a review of the current literature in this field by emphasising how DL has been applied to DR segmentation and lesion detection from fundus images. Deep learning is a section of artificial intelligence which uses artificial neural networks with many processing layers for steadily extracting high-level features from the data. This article is organized as follows: the studies that were reviewed associated with segmentation and lesion detection are shown in Section 2. Section 3 illustrates the datasets used. These are followed by an evaluation and discussion of these methods in Section 4. After this we have compiled a list of some possible future directions in this area of study in Section 5.