用于术前甲状旁腺分割的双分支特征增强变换器

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Lei Lyu , Chen Pang , Qinghan Yang , Kailin Liu , Chong Geng
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引用次数: 0

摘要

甲状腺手术中很容易损伤或意外切除甲状旁腺,从而导致暂时性甚至永久性低钙血症。因此,术前通过超声准确识别和定位甲状旁腺对于保护甲状旁腺和防止甲状腺手术中的甲状旁腺损伤至关重要。然而,目前只有少数几种方法能在甲状腺手术前通过超声图像突出显示甲状旁腺。在这项研究中,我们提出了一种用于术前甲状旁腺分割的双分支特征增强变换网络(DRT-Net)。DRT-Net 采用双分支结构,包括一个称为特征增强子网(FR-subnet)的设计卷积网络(CNN)主干和一个从混淆的超声图像中捕捉详细特征和上下文信息的变换器分支。此外,我们还设计了边缘跟踪注意(MTA),通过跟踪特征图的边缘像素来优化 FR 子网处理边缘信息的能力。最后,我们采用了跨通道特征增强模块(CFRM),将从 CNN 分支提取的细节特征与从 Transformer 分支提取的全局上下文信息进行融合。我们在自建的甲状旁腺分割数据集和开放访问的 Kvasir-SEG 数据集上对 DRT-Net 进行了训练和评估。为了验证我们方法的效率,我们进行了广泛的实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dual-branch feature Reinforcement Transformer for preoperative parathyroid gland segmentation
The parathyroid glands are easily injured or accidentally removed during thyroid surgery, causing temporary or even permanent hypocalcemia. Thus, accurate preoperative identification and localization of the parathyroid glands by ultrasound is crucial in protecting the parathyroid glands and preventing parathyroid injury during thyroid surgery. However, there are only a few methods used for highlighting the parathyroid gland in ultrasound images before thyroid surgery. In this study, we propose a Dual-branch feature Reinforcement Transformer Network (DRT-Net) for preoperative parathyroid gland segmentation. DRT-Net incorporates a dual-branch structure, consisting of a devised convolution network (CNN) backbone called Feature Reinforcement subnet (FR-subnet) and a Transformer branch capturing detailed features and context information from the confused ultrasound image. Furthermore, we design a Margin Tracking Attention (MTA) that optimizes the ability of FR-subnet to process margin information by tracking margin pixels of feature map. Finally, we employ a Cross-channel Feature Reinforcement Module (CFRM) to fuse the extracted detailed features from the CNN branch with the global context information from the Transformer branch. We trained and evaluated the DRT-Net on the self-built parathyroid gland segmentation dataset and an open-access Kvasir-SEG dataset. Extensive experiments have been carried out to validate the efficiency of our method.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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