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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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使用深度学习进行脑瘤检测和细分.

Rafia Ahsan1, Iram Shahzadi2,3, Faisal Najeeb4

  • 1Department of Electrical and Computer Engineering, Medical Image Processing Research Group (MIPRG), COMSATS University Islamabad, Islamabad, Pakistan.

Magma (New York, N.Y.)
|September 4, 2024
PubMed
概括
此摘要是机器生成的。

这项研究比较了用于脑瘤检测和细分的深度学习模型. 与2D U-Net相结合的YOLOv5在精确检测和细分脑瘤方面表现出卓越的性能.

关键词:
大脑瘤是什么?分类 分类 分类 分类.深度学习是一种深度学习.检测 检测 检测 检测 检测磁共振成像 (MRI) 是一种磁共振成像.分段化 分段化 分段化 分段化

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 神经瘤学神经瘤学

背景情况:

  • 脑瘤检测,分类和细分由于瘤异质性而复杂.
  • 深度学习对象检测算法对脑瘤数据的性能需要进一步探索.

研究的目的:

  • 在MRI数据上比较物体检测算法 (Faster R-CNN,YOLO,SSD) 进行脑瘤检测.
  • 将最好的检测网络与2D U-Net配对,以实现精确的瘤细分.

主要方法:

  • 对大脑瘤Figshare (BTF) 数据集的评估.
  • 用2D U-Net连接最好的对象检测网络进行细分.
  • 在BRATS 2018数据上微调检测网络,以检测和分类质瘤.

主要成果:

  • 在检测三种瘤类型方面,YOLOv5获得了最高的平均平均精度 (mAP) 89.5%,达到最高水平.
  • 在YOLOv5-2D U-Net组合中,对细分的子相似度系数 (DSC) 为88.1%,超过单独的2D U-Net (80.5%).
  • 拟议的方法在mAP (89.5%对67%) 和DSC (88.1%对44.2%) 两方面都超过了Mask R-CNN.

结论:

  • 建议将YOLOv5和2D U-Net结合在一起的深度学习方法用于多类脑瘤检测,分类和细分.
  • 拟议的方法可以准确检测各种脑瘤,并精确地划定瘤区域.