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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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使用优化的生成对抗网络 (GAN) 生成OCT B-Scan DME图像.

Aditya Tripathi1, Preetham Kumar1, Veena Mayya1

  • 1Department of Information & Communication Technology, Manipal Institute of Technology, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Heliyon
|August 23, 2023
PubMed
概括

本研究引入了一种生成对抗网络 (GAN) 模型,以创建现实的光学连贯断层扫描 (OCT) 图像,用于检测糖尿病黄斑 (DME). 这种方法旨在提高糖尿病眼病的诊断准确性和治疗策略.

关键词:
糖尿病是一种糖尿病.糖尿病性黄斑 (DME) 是一种疾病.生成对抗性网络 (GAN) 是一种产生对抗性的网络.光学连贯性断层扫描 (OCT) B-扫描

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科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 糖尿病黄斑 (DME) 在糖尿病患者中引起显著的视力损失.
  • 手动解释光学一致性断层扫描 (OCT) 的B扫描图像对DME是容易出错的.
  • 可靠的诊断方法对于有效的DME管理至关重要.

研究的目的:

  • 使用生成对抗网络 (GAN) 开发一个自动化的模型来合成DME OCT B-Scan图像.
  • 通过生成合成OCT图像来提高DME检测系统的稳定性.
  • 为了比较DME图像生成的五种不同的GAN架构的性能.

主要方法:

  • 使用了五种GAN:深度卷积GAN,条件GAN,循环GAN,样式GAN2和样式GAN3.
  • 从基线患者的OCT图像生成合成OCTB扫描图像.
  • 使用粒子集群优化 (PSO) 的最佳性能GAN的微调超参数.

主要成果:

  • 对五个GAN进行比较分析,以生成现实的DME OCT图像.
  • 确定用于合成高质量的OCT B-Scan图像的最佳性能GAN.
  • 已证明生成图像的潜力可以改善DME检测和严重性评估.

结论:

  • 拟议的基于GAN的模型为生成现实的DME OCT图像提供了一个有希望的方法.
  • 这种方法有可能提高DME诊断系统的准确性和可靠性.
  • 关于在眼科中选择合适的GAN用于合成OCT图像生成的见解.