Multi-task thyroid tumor segmentation based on the joint loss function. (January 2023)

• Record Type: Journal Article
• Authors: Yang, Dedong; Li, Yangyang; Yu, Jiankang
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 2
• Publication Date: 2023-01
• Subjects: Multi-task learning -- Thyroid tumor segmentation -- Joint loss function; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104249
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Medical image segmentation is one of the important topics in the field of medical image, which has attracted the great attention of researchers in recent years. However, there are few studies on the segmentation methods of thyroid tumors, which are determined by the characteristics of the thyroid and the mode of B-mode ultrasound imaging. Therefore, the segmentation of B-mode ultrasound images of thyroid tumors and the study of benign and malignant classification technology have important value and significance. In this paper, we propose a multi-task segmentation framework based on the joint loss function. First, based on our previous work, Feature Fusion Attention Network to Medical Image Segmentation (FFANet), we add a classification branch to expand it into a multi-task image segmentation framework. Then, we design the corresponding joint loss function and explore the weight coefficients between the two loss functions in the segmentation and classification tasks. Finally, through the experiment on thyroid tumor segmentation and classification dataset, the dice coefficient of the multi-task framework proposed in this paper is 0.935, and the classification accuracy is 0.790. It achieves competitive performance compared to existing methods. Highlights: We propose a multi-task thyroid tumor segmentation framework. We design the corresponding joint loss function. In the semantic segmentation task, the dice coefficient is 0.935.