College of Physics and Information Engineering, Fuzhou University; Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University.
College of Physics and Information Engineering, Fuzhou University; Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University; Imperial Vision Technology.
Comput Methods Programs Biomed. 2022 Dec;227:107186. doi: 10.1016/j.cmpb.2022.107186. Epub 2022 Oct 17.
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.
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.
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.
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.
甲状腺结节是甲状腺内异常生长的肿块，是甲状腺癌的早期症状。为了尽早诊断和治疗甲状腺癌，需要准确地对结节进行特征描述。由于存在斑点噪声、强度异质性、对比度低和分辨率低等问题，甲状腺超声结节分割是一项具有挑战性的任务。本文提出了一种新的框架来提高甲状腺结节分割的准确性。
与以往的工作不同，我们首先构建了一个超分辨率重建网络来提高输入超声图像的分辨率。之后，我们提出的 N 形网络用于执行分割任务。超分辨率重建网络的引导作用可以使输入甲状腺超声图像的高频信息比原始图像更丰富、更全面。我们的 N 形网络由几个空洞空间金字塔池化块、一个多尺度输入层、一个具有注意力块的 U 形卷积网络和一个新的并行空洞卷积（PAC）模块组成。这些模块有利于在多个尺度上捕获上下文信息，以便充分利用病变分割的语义特征。特别是，我们提出的 PAC 模块有利于从不同的感受野中提取高层语义特征，从而进一步提高分割性能。我们使用 UTNI-2021 数据集进行模型训练、验证和测试。
实验结果表明，我们提出的方法在 UTNI-2021 数据集上取得了 91.9%的 Dice 值、87.0%的 mIoU 值、88.0%的 Precision 值、83.7%的 Recall 值和 84.3%的 F1-score 值，优于大多数最先进的方法。
我们的方法在 UTNI-2021 数据集上取得了最佳性能，为超声图像分割提供了一种新方法。我们相信，我们的方法可以为临床实践中的医生提供可靠的辅助诊断信息。
