# U-Net-Based Semi-Automatic Semantic Segmentation Using Adaptive Differential Evolution
## 1 Introduction  
Medical image segmentation has recently been paid much attention, as its performance has improved to the level used in medical diagnostics, including retinal disorder [1; 2; 3; 4; 5], cancer [6; 7], and finger vein recognition [8]. In particular, tumor detection using computer tomography (CT) and magnetic resonance imaging (MRI) images can extract tumor regions roughly, so these techniques have reached practical uses [9; 10; 11]. By contrast, bone semantic segmentation needs precise pixelwise region detection to generate bone models accurately, so it can be used for medical diagnoses such as osteoporosis of the femur, which has a simple shape; this technique is not yet at a practical stage for use with the spine [12; 13]. As this demonstrates, the semantic segmentation of bones is still a challenging research topic. Semantic segmentation is a regional extraction problem that requires a semantic understanding of each pixel and precise accuracy. Before the advent of deep learning, various image processing methods based on clustering and boosting had been proposed. These methods required appropriate feature descriptions for semantic segmentation from various image features, which included data such as color tones and intensity, color palettes,shapes, and textures such as HOG, SURF, and SHIFT [14; 15; 16]. They also required these features to be optimally selected considering each subject image dataset. By contrast, many deep learning-based methods have been published based on general image datasets, such as those represented by ImageNet. A fully convolutional network (FCN) [17] was the first convolutional neural network (CNN)-based semantic segmentation method and replaced fully connected layers with a \(1\times 1\) convolution layer. An FCN is based on a pretrained VGG-16 model for image classification but has shown high segmentation performance. SegNet [18] is another CNN-based method, but it has an encoder network and a corresponding decoder network. The novelty of SegNet is the decoder, which uses pooling indices computed in the max-pooling step of the corresponding encoder to perform nonlinear upsampling. SegNet often extracts pedestrians, cars, buildings, and roads based on the Camid dataset. CRF-RNN [19] incorporates a conditional random field (CRF) to clarify detailed parts. Moreover, various other methods, such as Deep Mask [20], Fusion Net [21], and DeepLab versions [22; 23] have been proposed. These methods focus on segmentation performance for general images and learn based on a large number of image datasets. Unlike the methods outlined above, medical image segmentation aims for semantic pixelwise segmentation based on CT and MRI images. Compared to segmentation for general images, medical image segmentation requires highly accurate performance to diagnose a patient's condition. Early medical image segmentation methods were based on the above-mentioned image processing methods. As several public datasets have been published, CNN-based methods have been proposed [24]; however, it is difficult to obtain pixel-level medical image-labeled data, which are known as ground truths (GTs). For this reason, state-of-art models for image segmentation have not been adopted because most of them consist of deep layers and require huge datasets to optimize numerous weight parameters. A widely successful CNN-based model for medical imaging is the U-net [25]. The structure of the U-net is U-shaped and has a relatively compact architecture with skip connections. It can combine high-level semantic feature maps from the decoder and corresponding low-level detailed feature maps from the encoder. Most U-net-based medical image segmentation methods use benchmark datasets [26; 27]; however, each patient model must be constructed for an accurate diagnosis. Compared to other models, the U-net requires less training data, but the cost of creating GTs remains high. In general, GTs are created from precise handwritten boundary lines, generated one by one, for training in the U-net.