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基于深度网络的全面口腔腺瘤检测系统

Kubilay Muhammed Sunnetci¹, Esat Kaba², Fatma Beyazal Celiker²

¹Osmaniye Korkut Ata University, Department of Electrical and Electronics Engineering, Osmaniye 80000, Turkey (K.M.S.); Kahramanmaraş Sütçü İmam University, Department of Electrical and Electronics Engineering, Kahramanmaraş 46050, Turkey (K.M.S., A.A.).

Academic radiology

|June 4, 2023

概括

这项研究开发了一种人工智能模型,用于使用MRI图像的深度学习来自动细分状腺瘤. 该系统实现了高精度,为临床使用提供了手动细分的更快的替代方案.

关键词:

深度学习是一种深度学习.磁共振成像技术磁共振成像技术状腺瘤是什么意思分段化分段化分段化分段化

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科学领域:

放射学和医学成像学医学成像学
人工智能在医学中的应用
在瘤学瘤学.

背景情况:

唾液腺瘤很少见,占头部和部瘤的2%-6%,主要影响腺.
磁共振成像对于诊断状腺瘤,评估瘤特征和计划治疗至关重要.
腺瘤的手动细分是耗时的,需要专门的专业知识.

研究的目的:

开发和评估基于深度学习的状腺瘤 (PGT) 的自动细分模型.
为PGT细分创建一个用户友好的软件应用程序.
为了比较不同深度学习架构在各种MR图像对比度上对PGT细分的性能.

主要方法:

使用了102张T1-w,102张对比度增强的T1-w (T1C-w) 和102张T2-wMR图像的数据集.
基于ResNet18和基于Xception的DeepLab v3+的六个深度学习模型被训练和评估.
图像经过预处理,由专家手动细分,并分为培训 (80%) 和测试 (20%) 组.

主要成果:

在T1C-w图像上训练的基于ResNet18的DeepLab v3+架构实现了最高的精度 (0.96153) 和加权的交叉点在Union (0.92601).
与现有文献相比,开发的模型显示出具有竞争力的性能,并为T1-w,T1C-w和T2-w图像提供了独立的培训.
该研究成功开发了用于自动PGT细分的软件应用程序.

结论:

拟议的深度学习方法提供了一种高效和准确的方法,用于自动分离状腺瘤.
开发的软件应用程序可以显著减少与手动细分相关的工作量和成本.
这项研究为文献做出了有价值的贡献,有可能改善状腺瘤的诊断和治疗计划.