A Super-resolution Guided Network for Improving Automated Thyroid Nodule Segmentation
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Updated: Aug 23, 2025
Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
Published on: February 9, 2024
Xingtao Lin1, Xiaogen Zhou1, Tong Tong2
1College of Physics and Information Engineering, Fuzhou University; Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University.
Background And Objective:
A thyroid nodule is an abnormal lump that grows in the thyroid gland, which is the early symptom of thyroid cancer. In order to diagnose and treat thyroid cancer at the earliest stage, it is desired to characterize the nodule accurately. Ultrasound thyroid nodules segmentation is a challenging task due to the speckle noise, intensity heterogeneity, low contrast and low resolution. In this paper, we propose a novel framework to improve the accuracy of thyroid nodules segmentation.
Methods:
Different from previous work, a super-resolution reconstruction network is firstly constructed to upscale the resolution of the input ultrasound image. After that, our proposed N-shape network is utilized to perform the segmentation task. The guidance of super-resolution reconstruction network can make the high-frequency information of the input thyroid ultrasound image richer and more comprehensive than the original image. Our N-shape network consists of several atrous spatial pyramid pooling blocks, a multi-scale input layer, a U-shape convolutional network with attention blocks and a proposed parallel atrous convolution(PAC) module. These modules are conducive to capture context information at multiple scales so that semantic features can be fully utilized for lesion segmentation. Especially, our proposed PAC module is beneficial to further improve the segmentation by extracting high-level semantic features from different receptive fields. We use the UTNI-2021 dataset for model training, validating and testing.
Results:
The experimental results show that our proposed method achieve a Dice value of 91.9%, a mIoU value of 87.0%, a Precision value of 88.0%, a Recall value 83.7% and a F1-score value of 84.3%, which outperforms most state-of-the-art methods.
Conclusions:
Our method achieves the best performance on the UTNI-2021 dataset and provides a new way of ultrasound image segmentation. We believe that our method can provide doctors with reliable auxiliary diagnosis information in clinical practice.
