U-Net-Based Semi-Automatic Semantic Segmentation Using Adaptive Differential Evolution

Bone semantic segmentation is essential for generating a bone simulation model for automatic diagnoses, and a convolution neural network model is often applied to semantic segmentation. However, ground-truth (GT) images, which are generated based on handwriting borderlines, are required to learn this model. It takes a great deal of time to generate accurate GTs from handwriting borderlines, which is the main reason why bone simulation has not been put to practical use for diagnosis. With the above in mind, we propose the U-net-based semi-automatic semantic segmentation method detailed in this paper to tackle the problem. Moreover, bone computed tomography (CT) images are often presented in digital imaging and communications in medicine format, which consists of various parameters and affects the image quality for segmentation. We also propose a novel adaptive input image generator using an adaptive differential evolution. We evaluate the proposed method compared to conventional U-net and DeepLabv3 models using open bone datasets, the spine and the femur, and our artificial bone data. Performance evaluations show that the proposed method outperforms U-net and DeepLabv3 in terms of Dice, IoU, and pairwise accuracy, and DeepLabv3 show the lowest performance, due to a lack of training data. We verify that the U-net-based model is effective for bone segmentation, where a large quantity of training data are available. Moreover, we verify that the proposed method can effectively create proper GTs and input images, resulting in increased performance and reduced computational costs. We believe that the proposed method enhances the wide use of bone simulation based on CT images for practical use.