# Nailfold Microhemorrhage Segmentation with Modified U-Shape Convolutional Neural Network
## 1 Introduction  
Nailfold capillaroscopy (NC) provides easy access to microcirculation, which is of significant importance in analyzing several cardiovascular and rheumatic diseases. It is safe, inexpensive, noninvasive, and used as a standard clinical practice for the detection of nailfold abnormalities [1]. Common nailfold abnormalities include enlarged (giant) capillaries, microhemorrhages, loss of capillaries, disorganization of the vascular array, and bushy capillaries [2; 3; 4].  
Nailfold microhemorrhages are defined as extravasation of red blood cells into the perivascular tissue as red or brown aggregations [5]. It is associated with the damage of the vessel wall and usually reflects the injury of capillaries. In particular, nail microhemorrhages may first manifest in nailfold microhemorrhages [6]. As illustrated in Figure 1, red cells leave the damaged capillaries and form punctuate hemorrhages and areas of the confluent hemorrhages [7].  
To observe the evolvement of microhemorrhages and analyze the damage of vascular \(\pi\), it is crucial to establish an accurate nailfold microhemorrhage segmentation system. Solutions for a precise assessment of microhemorrhages can be valuable in various situations, such as diagnosis of diabetic retinopathy or optic disc hemorrhage present in glaucoma.  
Diabetic retinopathy (DR) is the most common microvascular complication of diabetes and it remains a major cause of preventable blindness [8]. Most patients show no symptoms at the early stage of DR, which makes it difficult for clinical screening. However, an association was reported between diabetes and several capillariesocip abnormalities including microthermages [7; 8; 9; 10]. Beyond DR, nailfold microthermages are also closely related to optic disc hemorrhages found in both normal-tension glaucoma (NTG) and primary open-angle glaucoma (POAG) patients [6; 10; 11] and thus provide valuable pathological information. These findings show the prognostic importance of nailfold microthermor-rhages for diagnosing optic disc hemorrhage, which is a clinical risk factor for glaucoma progression [12; 13].  
Recently, deep learning has achieved great success in numerous visual recognition tasks, including medical image segmentation [14]. Successful methods such as FCN (fully convolutional networks) [15], U-Net [16], and DSN (deeply-supervised nets) [17] have reached a high quality in terms of common evaluation metrics. Therefore, we address this task as a pixelwise classification of the input NC images and solve it with a novel U-shape convolutional neural network. The main contributions of our work include:  
1. An end-to-end deep learning model is proposed for nailfold microthermorhage segmentation using a U-shape network named DAFM-Net. DAFM-Net inherits the advantages of U-Net and generates accurate probability maps for NC images.
2. A dual attention fusion module is designed to emphasize the informative features while suppressing the trivial ones. Spatial and channel information are captured and then thoroughly aggregated by a feature fusion module to further improve feature representation, which contributes to more precise pixelwise classification.
3. Instead of batch normalization, we employ group normalization for regularization and obtain satisfactory optimization performance.
4. At last, we verify the effectiveness of DAFM-Net on a newly collected dataset and achieve remarkable success. Furthermore, ablation experiments are also conducted to discuss the necessity of group normalization and to evaluate the representation power of our dual attention fusion module.