Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada.
Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
Comput Biol Med. 2023 May;157:106792. doi: 10.1016/j.compbiomed.2023.106792. Epub 2023 Mar 21.
Segmentation of anatomical structures in ultrasound images is a challenging task due to existence of artifacts inherit to the modality such as speckle noise, attenuation, shadowing, uneven textures and blurred boundaries. This paper presents a novel attention-based predict-refine network, called ACUE-Net, for segmentation of soft-tissue structures in ultrasound images. The network consists of two modules: a predict module, which is built upon our newly proposed attentive coordinate convolution; and a novel multi-head residual refinement module, which consists of three parallel residual refinement modules. The attentive coordinate convolution is designed to improve the segmentation accuracy by perceiving the shape and positional information of the target anatomy. The proposed multi-head residual refinement module reduces both segmentation biases and variances by integrating residual refinement and ensemble strategies. Moreover, it avoids multi-pass training and inference commonly seen in ensemble methods. To show the effectiveness of our method, we collect a comprehensive dataset of thyroid ultrasound scans from 12 different imaging centers, and evaluate our proposed network against state-of-the-art segmentation methods. Comparisons against state-of-the-art models demonstrate the competitive performance of our newly designed network on both the transverse and sagittal thyroid images. Ablation studies show that proposed modules improve the segmentation Dice score of the baseline model from 79.62% to 80.97% and 82.92% while reducing the variance from 6.12% to 4.67% and 3.21% in transverse and sagittal views, respectively.
超声图像中解剖结构的分割是一项具有挑战性的任务，这是由于该模态存在固有伪影，例如斑点噪声、衰减、阴影、不均匀纹理和模糊边界。本文提出了一种新颖的基于注意力的预测-细化网络 ACUE-Net，用于超声图像中软组织结构的分割。该网络由两个模块组成：一个预测模块，它建立在我们新提出的注意坐标卷积之上；和一个新的多头残差细化模块，它由三个并行的残差细化模块组成。注意坐标卷积旨在通过感知目标解剖结构的形状和位置信息来提高分割精度。所提出的多头残差细化模块通过集成残差细化和集成策略来减少分割偏差和方差。此外，它避免了集成方法中常见的多步训练和推理。为了展示我们方法的有效性，我们从 12 个不同的成像中心收集了全面的甲状腺超声扫描数据集，并将我们提出的网络与最先进的分割方法进行了评估。与最先进的模型进行比较表明，我们新设计的网络在横向和矢状甲状腺图像上都具有竞争力。消融研究表明，所提出的模块将基线模型的分割 Dice 得分从 79.62%提高到 80.97%和 82.92%，同时将横向和矢状视图中的方差从 6.12%降低到 4.67%和 3.21%。
