DMU-Net: Dual-route mirroring U-Net with mutual learning for malignant thyroid nodule segmentation. (August 2022) - Yang, Qinghan; Geng, Chong; Chen, Ruyue; Pang, Chen; Han, Run; Lyu, Lei; Zhang, Yuang
Highlights: We propose a novel DMU-Net, which is mainly composed of two specific branches connected by the AM for assisting doctors in diagnosing malignant thyroid nodules more accurate. We introduce the PAM and MRM into the two subnets respectively to capture the contextual information and fine margin features from thyroid nodule ultrasonic image. We build a new malignant thyroid nodule segmentation (MTNS) dataset to evaluate the segmentation performance of the DMU-Net. We introduce the mutual learning strategy into the biomedical image segmentation to further improve the segmentation precision of DMU-Net. Abstract: It is meaningful for radiologists to segment thyroid nodules in ultrasound images quickly and accurately using an effective segmentation algorithm. With the rise of deep learning in computer vision, many deep learning-based methods have been proposed to assist radiologists in diagnosing thyroid diseases, such as thyroid nodule classification, detection and segmentation, but there exist few methods paying attention to malignant thyroid nodule segmentation. The goal of thyroid nodule segmentation is to identify the type of thyroid nodule. However, the identification of thyroid nodule type has been relatively well developed and the identification work almost can't bother radiologists. The more important for radiologists is to detect the inconspicuous malignant nodules precisely in ultrasonic images, avoiding radiologists confusing tissues and malignant thyroid nodules during their diagnosis. This paper proposes a deep learning-based CAD (Computer-aided diagnosis) method called Dual-route Mirroring U-Net (DMU-Net) to segment malignant thyroid nodules automatically. The method uses two subnets (U-shape subnet, inversed U-shape subnet) and three modules (pyramid attention module (PAM), margin refinement module (MRM), aggregation module (AM)) to extract contextual information of thyroid nodules and margin details in ultrasonic images. Further, the strategy of mutual learning is introduced from the natural image classification task to enhance the performance of DMU-Net. We train and evaluate our method on the self-built Malignant Thyroid Nodule Segmentation (MTNS) dataset. Finally, we compare the DMU-Net with several classical deep learning-based methods on the MTNS dataset and other public datasets. The results show our DMU-Net can achieve superior performance on these datasets.