# Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation
## 1 Introduction  
Image semantic segmentation is one of the core tasks in the field of computer vision. From the microscopic point of view, semantic segmentation can be understood as classifying each pixel in the image. It is a pixel-level space-intensive prediction task. In other words, semantic segmentation attempts to understand the meaning of each pixel in the image semantically, such as identifying whether it is a car, a building, or a pedestrian. From a macro point of view, semantic segmentation can be seen as assigning consistent semantic labels to a class of things instead of each pixel. It has important application value in video surveillance, automatic driving, scene understanding, and 3D scene modeling. Interactive image segmentation methods include Graphcut [1] and Grabcut [2]. Graphcut uses the minimum-cut maximum-flow algorithm for image segmentation, which can segment the image into the foreground and background. When using this algorithm, it is necessary to draw a few strokes at the foreground and background as input. The algorithm will establish a weighted map of the similarity between each pixel and the foreground and background, and distinguish the foreground and background by solving the minimum cut. Grabcut algorithm is an iterative algorithm of the Graphcut method, which utilizes the color information and boundary information in the RGB image, and manually selects the area of interest. The inside of the frame is regarded as the unknown area, and the outside of the frame is regarded as the background area. A GMM (Gaussian Mixture Model) is established between the unknown area and the calibrated background area, the foreground GMM and the background GMM are initialized according to the result of the manual selection, and then the initialized area (unknown area) is divided into the target and the background area. Early research on fully automatic semantic segmentation were dominated by traditional methods such as the threshold method [3], the edge detection-based method [4],the conditional random field (CRF)-based method [5], and the cluster-based method [6]. Among them, the threshold method uses a single threshold for direct classification, which is mainly applied to the segmentation of grayscale images, such as Otsu [7], which belongs to an adaptive threshold determination method. The image fixed threshold binarization with this threshold has the largest inter-class variance. It divides the image into background and foreground according to the gray characteristics of the image so that the segmentation with the largest inter-class variance means the minimum misclassification probability. The core idea is to find a threshold, T, which divides all pixels of the image into two categories. The pixel values of one category are less than or equal to T (background area), and the pixel values of the other category are greater than T (foreground area). When the variance between these two categories reaches the maximum, the T value is considered to be the most appropriate threshold. The edge detection-based method classifies objects by boundary information. The CRF method is a probabilistic framework for labeling and segmentation; the interaction process between pixels can be modeled, and the efficiency is usually higher than the threshold method and edge detection method. The clustering-based method divides the image into K groups by clustering pixels or regions with similar characteristics; K-means clustering is the mainstream method. In the past few years, medical image segmentation has attracted increasing attention, the purpose of which is to separate the parts with special meanings from medical images. In the medical field, doctors mainly rely on medical film images and subjective judgments to diagnose diseases. Medical image segmentation is a key step in medical 3D reconstruction and quantitative analysis. It is also an important technical premise for quantification of lesion areas, selection of treatment methods, and radiotherapy.