Dong Peizhen, Zhang Ronghua, Li Jun, Liu Changzheng, Liu Wen, Hu Jiale, Yang Yongqiang, Li Xiang
College of Information Science and Technology, Shihezi University, Shihezi, 832003, Xinjiang, China.
Department of Medical Ultrasound, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi, 832003, Xinjiang, China.
BMC Med Imaging. 2024 Dec 18;24(1):341. doi: 10.1186/s12880-024-01521-z.
This study aims to design an auxiliary segmentation model for thyroid nodules to increase diagnostic accuracy and efficiency, thereby reducing the workload of medical personnel.
This study proposes a Dual-Path Attention Mechanism (DPAM)-UNet++ model, which can automatically segment thyroid nodules in ultrasound images. Specifically, the model incorporates dual-path attention modules into the skip connections of the UNet++ network to capture global contextual information in feature maps. The model's performance was evaluated using Intersection over Union (IoU), F1_score, accuracy, etc. Additionally, a new integrated loss function was designed for the DPAM-UNet++ network.
Comparative experiments with classical segmentation models revealed that the DPAM-UNet++ model achieved an IoU of 0.7451, an F1_score of 0.8310, an accuracy of 0.9718, a precision of 0.8443, a recall of 0.8702, an Area Under Curve (AUC) of 0.9213, and an HD95 of 35.31. Except for the precision metric, this model outperformed the other models on all the indicators and achieved a segmentation effect that was more similar to that of the ground truth labels. Additionally, ablation experiments verified the effectiveness and necessity of the dual-path attention mechanism and the integrated loss function.
The segmentation model proposed in this study can effectively capture global contextual information in ultrasound images and accurately identify the locations of nodule areas. The model yields excellent segmentation results, especially for small and multiple nodules. Additionally, the integrated loss function improves the segmentation of nodule edges, enhancing the model's accuracy in segmenting edge details.
本研究旨在设计一种用于甲状腺结节的辅助分割模型，以提高诊断的准确性和效率，从而减轻医务人员的工作量。
本研究提出了一种双路径注意力机制（DPAM）-UNet++模型，该模型可自动分割超声图像中的甲状腺结节。具体而言，该模型将双路径注意力模块纳入UNet++网络的跳跃连接中，以捕获特征图中的全局上下文信息。使用交并比（IoU）、F1分数、准确率等对模型性能进行评估。此外，还为DPAM-UNet++网络设计了一种新的综合损失函数。
与经典分割模型的对比实验表明，DPAM-UNet++模型的IoU为0.7451，F1分数为0.8310，准确率为(0.9718)，精确率为0.8443，召回率为0.8702，曲线下面积（AUC）为0.9213，95%豪斯多夫距离（HD95）为35.31。除精确率指标外，该模型在所有指标上均优于其他模型，且分割效果与真实标签更为相似。此外，消融实验验证了双路径注意力机制和综合损失函数的有效性和必要性。
本研究提出的分割模型能够有效捕获超声图像中的全局上下文信息，并准确识别结节区域的位置。该模型产生了优异的分割结果，尤其是对于小的和多发的结节。此外，综合损失函数改善了结节边缘的分割，提高了模型在分割边缘细节方面的准确性。
