Name: a swin transformer-based model for thyroid nodule detection in ultrasound images
Uploaded: 2023-04-21T14:15:00
Description: Watch this Scientific Journal Video about A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images at JoVE.com

本研究得到了国家自然科学基金（批准号：32101188）和四川省科技厅科技厅重点项目（批准号：2021YFS0102）的支持。

这项回顾性研究得到了中国四川大学华西医院机构审查委员会的批准，并免除了获得知情同意的要求。
1. 环境设置
<ol><li>图形处理单元 （GPU） 软件<ol><li>要实现深度学习应用程序，请先配置 GPU 相关环境。从 GPU 的网站下载并安装 GPU 适当的软件和驱动程序。 注意：有关本研究中使用的材料，请参阅 材料表 。</li>
	</ol></li>
	<li>Python3.8 安装<ol><li>?...

<ol>
	<li>Grant, E. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thyroid+ultrasound+reporting+lexicon:+White+paper+of+the+ACR+Thyroid+Imaging,+Reporting+and+Data+System+(TIRADS)+committee.">Thyroid ultrasound reporting lexicon: White paper of the ACR Thyroid Imaging, Reporting and Data System (TIRADS) committee.</a> Journal of the American College of Radiology. 12 (12 Pt A), 1272-1279 (2015).</li><li>Zhao, J., Zheng, W., Zhang, L., Tian, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segmentation+of+ultrasound+images+of+thyroid+nodule+for+assisting+fine+needle+aspiration+cytology.">Segmentation of ultrasound images of thyroid nodule for assisting fine needle aspiration cytology.</a> Health Information Science and Systems. 1, 5 (2013).</li><li>Haugen, B. 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S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Observer+variability+in+the+sonographic+evaluation+of+thyroid+nodules.">Observer variability in the sonographic evaluation of thyroid nodules.</a> Journal of Clinical Ultrasound. 38 (6), 287-293 (2010).</li><li>Kim, S. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Observer+variability+and+the+performance+between+faculties+and+residents:+US+criteria+for+benign+and+malignant+thyroid+nodules.">Observer variability and the performance between faculties and residents: US criteria for benign and malignant thyroid nodules.</a> Korean Journal of Radiology. 11 (2), 149-155 (2010).</li><li>Choi, Y. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+computer-aided+diagnosis+system+using+artificial+intelligence+for+the+diagnosis+and+characterization+of+thyroid+nodules+on+ultrasound:+initial+clinical+assessment.">A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of thyroid nodules on ultrasound: initial clinical assessment.</a> Thyroid. 27 (4), 546-552 (2017).</li><li>Chang, T. -. 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本文详细介绍了如何进行环境设置、数据准备、模型配置和网络训练。在环境设置阶段，需要注意确保依赖库兼容和匹配。数据处理是非常重要的一步;必须花费时间和精力来确保注释的准确性。训练模型时，可能会遇到“ModuleNotFoundError”。在这种情况下，有必要使用“pip install”命令来安装缺少的库。如果验证集的损失没有减少或振荡很大，则应检查注释文件并尝试调整学习率和批量大小以使损...

自1970年以来，甲状腺癌的发病率迅速增加，特别是在中年妇女中1。甲状腺结节可能预示甲状腺癌的出现，大多数甲状腺结节无症状2.早期发现甲状腺结节对治愈甲状腺癌很有帮助。因此，根据现行实践指南，所有体格检查疑似结节性甲状腺肿或影像学检查结果异常的患者均应进一步检查3，4。
甲状腺超声（US）是用于检测和表征甲状腺病变的常用方法5，6。美国是一种方便、廉价且无辐射的技术。但是，美国的应用很容易受到运营商7，8的影响。甲状腺结节的形状、大小、回声度和质地等特征在美国图像上很容易区分。尽管某些 US 特征（钙化、回声和边界不规则）通常被认为是识别甲状腺结节的标准，但观察者间变异性的存在是不可避免的8，9。不同经验水平的放射科医生的诊断结果是不同的。没有经验的放射科医生比有经验的放射科医生更容易误诊。US 的某些特征（如反射、阴影和回波）会降低图像质量。由于美国成像的性质，图像质量的下降使得即使是经验丰富的医生也很难准确定位结节。
甲状腺结节的计算机辅助诊断（CAD）近年来发展迅速，可以有效减少不同医生造成的错误，帮助放射科医生快速准确地诊断结节10，11。已经提出了各种基于CNN的CAD系统用于甲状腺US结节分析，包括分割12，13，检测14，15和分类16，17。CNN是一个多层的监督学习模型18，CNN的核心模块是卷积层和池化层。卷积层用于特征提取，池化层用于缩减采样。阴影卷积层可以提取纹理、边缘和轮廓等主要特征，而深度卷积层可以学习高级语义特征。
CNN在计算机视觉19，20，21方面取得了巨大的成功。然而，由于卷积层的有效感受野有限，CNN无法捕获长期上下文依赖关系。过去，用于图像分类的骨干架构大多使用CNN。随着视觉变压器（ViT）22，23的出现，这一趋势发生了变化，现在许多最先进的型号都使用变压器作为骨干。基于非重叠的图像块，ViT使用标准变压器编码器25对空间关系进行全局建模。Swin Transformer24 进一步引入了换档窗口来学习功能。移位窗口不仅带来了更高的效率，而且大大减少了序列的长度，因为自我注意力是在窗口中计算的。同时，两个相邻窗口之间的交互可以通过移位（移动）的操作进行。Swin变压器在计算机视觉中的成功应用导致了对基于变压器的超声图像分析架构的研究26。
最近，Li等人提出了一种深度学习方法28 ，用于甲状腺状癌检测，其灵感来自Faster R-CNN27。更快的 R-CNN 是一种经典的基于 CNN 的对象检测架构。最初的Faster R-CNN有四个模块 - CNN骨干网，区域建议网络（RPN），ROI池层和检测头。CNN 主干网使用一组基本的 conv+bn+relu+pooling 层从输入图像中提取特征图。然后，特征图被馈送到 RPN 和 ROI 池层。RPN 网络的作用是生成区域提案。此模块使用 softmax 来确定定位点是否为正，并通过边界框回归生成准确的定位点。ROI 池化层通过收集输入的特征图和提案来提取提案特征图，并将提案特征图馈送到后续的检测头中。检测头利用建议特征图对物体进行分类，通过边界框回归获得检测箱的准确位置。
本文提出了一种新的甲状腺结节检测网络Swin Faster R-CNN，该网络是通过用Swin变压器替换Faster R-CNN中的CNN骨干而形成的，从而更好地从超声图像中提取结节检测的特征。此外，利用特征金字塔网络（FPN）29 ，通过聚合不同尺度的特征，提高模型对不同尺寸结核的检测性能。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>GPU RTX3090</td>
			<td>Nvidia</td>
			<td>1</td>
			<td>24G GPU</td>
		</tr>
		<tr>
			<td>mmdetection2.11.0</td>
			<td>SenseTime</td>
			<td>4</td>
			<td>https://github.com/open-mmlab/mmdetection.git</td>
		</tr>
		<tr>
			<td>python3.8</td>
			<td>&#8212;</td>
			<td>2</td>
			<td>https://www.python.org</td>
		</tr>
		<tr>
			<td>pytorch1.7.1</td>
			<td>Facebook</td>
			<td>3</td>
			<td>https://pytorch.org</td>
		</tr>
	</tbody>
</table>

a swin transformer-based model for thyroid nodule detection in ultrasound images

近年来，甲状腺癌的发病率一直在上升。甲状腺结节检测对于甲状腺癌的检测和治疗至关重要。卷积神经网络（CNN）在甲状腺超声图像分析任务中取得了良好的效果。然而，由于卷积层的有效感受野有限，CNN无法捕获长程上下文依赖性，这对于识别超声图像中的甲状腺结节非常重要。变压器网络在捕获远程上下文信息方面非常有效。受此启发，我们提出了一种新的甲状腺结节检测方法，该方法结合了Swin变压器骨架和更快的R-CNN。具体来说，超声图像首先被投影到一维嵌入序列中，然后将其馈送到分层的Swin变压器中。 
Swin变压器主干网通过利用偏移窗口计算自我注意来提取五种不同尺度的特征。随后，使用特征金字塔网络（FPN）对不同尺度的特征进行融合。最后，使用检测头来预测边界框和相应的置信度分数。从2，680名患者收集的数据用于进行实验，结果表明该方法获得了44.8%的最佳mAP评分，优于基于CNN的基线。此外，我们获得了比竞争对手更好的灵敏度（90.5%）。这表明该模型中的上下文建模对甲状腺结节检测有效。

2008年9月至2018年2月在中国两家医院采集的美国甲状腺图像。将美国图像纳入本研究的合格标准是活检和手术治疗前的常规美国检查、活检或术后病理学诊断以及年龄≥18岁。排除标准是没有甲状腺组织的图像。
3，000张超声图像包括1，384个恶性结节和1，616个良性结节。大多数（90%）恶性结节为状癌，66%的良性结节为结节性甲状腺肿。在这里，25%的结节小于5毫米，38%在5毫米至...

Watch this Scientific Journal Video about A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images at JoVE.com

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

该文提出一种超声图像甲状腺结节检测新模型，以Swin变压器为骨干进行远程上下文建模。实验证明，它在灵敏度和准确性方面表现良好。

基于Swin变压器的超声图像甲状腺结节检测模型

In recent years, the incidence of thyroid cancer has been increasing. Thyroid nodule detection is critical for both the detection and treatment of thyroid ...

该计划实施自动和有效的措施来检测甲状腺结节，这可能会减少医生将来的工作量。该技术将Swin变压器应用于结核检测网络。Swin变压器可有效捕获远距离内容信息，对提高结核滞留的灵敏度非常有帮助。首先，在机器上打开一个终端，通过在命令行中键入sudo apt-get install python3.8来安装Python 3.8。要安装 PyTorch 1.7，请访问 Miniconda 的官方网站并安装它。然后，通过在命令行中键入屏幕上显示的命令来创建Conda环境，并通过键入conda activate SwinFasterRCNN来激活它。通过在命令行中编写所示命令来安装 PyTorch。对于 MM 检测安装，请从官方 GitHub 存储库克隆，然后通过在命令行中编写命令 pip install v e 来安装 MM 检测。打开schedule_1x。py 文件，然后编辑该文件以设置默认的训练相关参数，包括优化器、学习率和史诗。通过键入屏幕上显示的命令开始训练。等待网络开始训练 48 个史诗，并在输出文件夹中生成生成的 SwinFasterRCNN 网络的训练权重。在验证集上以最高精度保存模型权重。训练后，在新图像中选择在甲状腺结节检测验证集上性能最佳的模型。运行测试脚本，将图像大小调整为 512 x 512 像素并规范化。等待脚本自动将预训练的模型参数加载到 SwinFasterRCNN，并将预处理的图像馈送到 SwinFasterRCNN 中进行推理。等待 SwinFasterRCNN 输出每个图像的预测框。最后，允许脚本自动对每个图像执行 NMS 后处理，以删除重复的检测框。同一图像的检测结果显示在给定行中。这些列是 SwinFasterRCNN、FasterRCNN、YOLOv3、SSD、RetinaNet 和 DETR 的检测结果。这些区域的基本实况用绿色矩形框标记。检测结果由红色矩形框。选择合适的超参数以确保收敛是非常重要的，这需要通过许多实验来确定。

Research

JoVE Journal

Medicine

Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound

利用磁共振成像，容积电脑断层扫描及超音波的裸鼠乳腺癌骨转移大鼠模型血管生成的多模态成像

Angiogenesis is an essential feature of cancer growth and metastasis formation. In bone metastasis, angiogenic factors are pivotal for tumor cell ...

科研

医学

Cerenkov Luminescence Imaging (CLI) for Cancer Therapy Monitoring

Cerenkov Luminescence Imaging CLI for Cancer Therapy Monitoring

癌症治疗监测的切伦科夫发光成像（CLI）

In  molecular imaging, positron emission tomography (PET) and optical imaging (OI)  are two of the most important and thus most widely used modalities1-3. ...

An Orthotopic Mouse Model of Anaplastic Thyroid Carcinoma

甲状腺未分化癌的原位小鼠模型

Several types of animal models of human thyroid carcinomas have been established, including subcutaneous xenograft and orthotopic implantation of cancer ...

Substernal Thyroid Biopsy Using Endobronchial Ultrasound-guided Transbronchial Needle Aspiration

胸骨后甲状腺活检使用支气管内超声引导下经支气管针吸

Substernal thyroid goiter (STG) represents about 5.8% of all mediastinal lesions1. There is a wide variation in the published incidence rates due to the ...

Intra-Operative Neural Monitoring of Thyroid Surgery in a Porcine Model

猪手术模型对甲状腺手术的手术内神经监测

Intraoperative injury to the recurrent laryngeal nerve (RLN) can cause vocal cord paralysis, which interferes with speech and can potentially interfere ...

Isometric Contractility Measurement of the Mouse Mesenteric Artery Using Wire Myography

用线 Myography 测定小鼠肠系膜动脉的等轴收缩力

The wire myograph technique is used to assess the contractility of vascular smooth muscles in response to depolarization, GPCR agonists/inhibitors and ...

Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery

电磁导航经胸结节定位用于微创胸外科手术

The increased use of chest computed tomography (CT) has led to an increased detection of pulmonary nodules requiring diagnostic evaluation and/or ...

A Pleural Effusion Model in Rats by Intratracheal Instillation of Polyacrylate/Nanosilica

聚丙烯酸/纳米水的气管内灌注积液模型

Pleural effusion is a prevalent clinical finding of many pulmonary diseases. Having a useful animal pleural effusion model is very important to study ...

A Personalized 3D-Printed Model for Preoperative Evaluation in Thyroid Surgery

用于甲状腺手术术前评估的个性化3D打印模型

The anatomic structure of the surgical area of thyroid cancer is complex. It is very important to comprehensively and carefully evaluate the tumor ...

Point-of-Care Ultrasound: A Review of Ultrasound Parameters for Predicting Difficult Airways

床旁超声：预测困难气道的超声参数综述

Airway management remains a crucial part of perioperative care. The conventional approach to assessing potentially difficult airways emphasizes the LEMON ...