# All You Need Is a Few Dots to Label CT Images for Organ Segmentation
## 1 Introduction  
Image segmentation is used to analyze the medical image quantitatively for the quantification of tissue volume, diagnosis, treatment planning, and computer-integrated surgery [1]. However, it takes a lot of time and effort for a radiologist and doctor to conduct segmentation on CT images of each patient. Therefore, the necessity for technology that can accelerate segmentation on CT images has been highlighted consistently.  
Over the past few decades, CNN-based deep learning techniques have made remarkable success in computer vision tasks, and there have been attempts to apply CNN-based methods in the field of medical image segmentation [2; 3; 4; 5; 6]. Afterward, UNet [7], which has the U-shaped encoder-decoder architecture with skip-connection, significantly improved the performance of the medical image segmentation task. Since the introduction of UNet, many variants using its architecture have been studied for segmentation tasks on organs and muscles of CT images in the medical field.  
Although many deep learning models perform well enough to generate segmentation on CT images, it is not easy for medical institutions to train a network fit for their purpose. First, making public labeled data is difficult because the privacy of local data in medical institutions is essential. In addition, it cannot be guaranteed that the network trained by public data will conduct segmentation well on data owned by each institution since the distribution of pixels in the CT image differs depending on radiography equipment. Finally, the tedious work of labeling is needed whenever a segmentation task is conducted on an unlabeled organ in a public dataset.  
In this research, we propose a deep-learning-based tool that can help with the labeling task, which is required to make the labeled data for a network performing segmentation and reduce the time and effort of experts. The network used for our tool can be trained with a small number of labeled data. After training, the tool shows the region of the organ when the user puts a dot on the target organ to be segmented. The user can conveniently adjust the threshold so that the labeling can be performed flexibly according to the data distribution. We expect that the labeled data for the medical image segmentation model would be easily made using our proposed labeling tool.  
We compared our tool with two representative segmentation methods, UNet and DeepLabV3, for evaluation. Our technique is challenging to compare with the automated segmentation methods because a user has to annotate the pixels and set the threshold. Therefore, we proceed with two preceding experiments to determine the number of input anchors and the threshold for the experiment. For an efficient experiment, the anchors are extracted from the ground truth. Finally, Dice Score and Hausdorff Distance are used to measure the performance.  
In this paper, our main contributions are as follows:  
* We propose a novel labeling tool that can be trained with only a few labeled data by incorporating visual features and locality information extracted from Feature Encoder and Gaussian kernel.
* We utilize anchor pixels from user interactions for easily segmenting organs. The pixels can be selected anywhere on the target organs, with no additional constraints, such as annotating bounding-box, extreme points.
* Our tool provides an additional function to refine the segmentation result in detail by modifying the threshold value.  
The following section discusses recent deep-learning-based segmentation models, interactive medical image segmentation models, and differences between the proposed method and existing interactive segmentation models. Section 3 introduces the proposed method, the dataset used for training, the metric for performance evaluation, and the application that can conveniently perform segmentation using our model. The results of measuring the performance of our method are presented in Section 4. Finally, Section 5 summarizes the proposed method and discusses future research directions.