# nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
## 1 Introduction  
Diabetic retinopathy (DR) is a common and potentially blinding eye disease and is one of the leading causes of blindness in adults in developed countries. Early diagnosis and treatment are crucial in preventing the progression of diabetic retinopathy. Many healthcare institutions have successfully reduced the visual impairment caused by DR and lowered its blindness rate through the promotion of early screening and timely medical interventions [1, 2]. However, the rapid increase in the number of DR patients still poses new challenges to public health and healthcare systems.  
The main lesions associated with DR include hard exudates (EXs), soft exudates (SEs), hemorrhages (HEs), and microaneurysms (MAs). EXs are lipid deposits that remain after the gradual absorption of extravasated plasma substances due to abnormal permeability of the retinal capillary walls. Typically, EXs manifest as yellow-white or white spots or patches with clear boundaries [3, 4]. SES are a result of occlusion and damage to retinal microvessels, causing severe ischemia and hypoxia in the nourished tissue. Typically, SEs manifest as faint yellow or off-white cotton-like patches with blurred edges [5, 6, 7]. HEs are mainly located in the deep layers of the retina and can appear as dot-like or patch-like formations, resulting from damaged small blood vessels. Typically, HEs manifest as red, dark-red, or deep-colored spots or patches, varying in size and shape [8, 9]. MAs are a typical early sign of DR, characterized by localized dilations and bulges of the retinal small blood vessel walls. Typically, MAs manifest as small red or orange circular or elliptical structures, commonly distributed in the central area of the retina [10, 11, 12, 13]. Based on the above-mentioned features, it becomes evident that the color and shape of these lesions in retinal images can easily lead to confusion. For instance, EXs and HEs share similar colors, and HEs and MAs display close resemblances to each other in both color and morphology. MAs, which appear on the vessel walls, also have a color resembling the vessels themselves. Presently, the mainstream method for diagnosing DR still relies on ophthalmologists manually examining fundus images of patients. However, due to thecomplexity and diversity of DR in retinal images, several issues arise when relying solely on manual diagnosis by eye care professionals. Firstly, manual diagnosis is susceptible to the influence of subjective judgments by doctors, and the diagnostic outcomes are constrained by their experience and expertise. Secondly, manual diagnosis is prone to misdiagnosis and oversights, particularly concerning subtle lesions that might be overlooked or misjudged. Lastly, manual diagnosis struggles to keep up with the demands of large-scale image data analysis and diagnostic requirements. Therefore, it is urgent and necessary to develop an automated detection method that utilizes computer technology to assist ophthalmologists with clinical diagnosis.  
To address the challenging task of segmenting DR in fundus images and to develop an accurate segmentation method, we proposed a model named nmODE-Unet, which is based on nmODE and U-net backbone. The contributions were summarized as follows:  
1. We proposed an nmODE to address the difficulty of segmenting DR lesions in fundus images. The nmODE has a unique global attractor, which exhibits strong resistance to noise, facilitating the extraction of lesion features.
2. We have constructed a new segmentation framework named nmODE-Unet. Experimental results on two different datasets demonstrate that nmODE-Unet exhibits an excellent performance in DR lesion segmentation tasks and displays strong robustness.