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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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深度学习算法用于多参数MRI上的膀癌细分.

Kazim Z Gumus1, Julien Nicolas2, Dheeraj R Gopireddy1

  • 1Department of Radiology, College of Medicine-Jacksonville, University of Florida, Jacksonville, FL 32209, USA.

Cancers
|July 13, 2024
PubMed
概括
此摘要是机器生成的。

深度学习模型MAnet和PSPnet在MRI扫描上显示了膀癌细分的改善. 使用交叉和子相似系数损失函数的组合,可以提高各种成像序列的性能.

关键词:
马尼特 (MAnet) 是一个网络.这就是为什么MRI是MRI.在PSPnet上使用PSPnet.乌内特网络 乌内特网络膀癌:膀癌是一种癌症.交叉的交叉.深度学习是一种深度学习.预期的校准错误预期的校准错误焦点损失是因为焦点损失.功能损失的功能损失的功能.细分化 细分化的细分化

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科学领域:

  • 放射学 放射学是一门学科.
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 在磁共振成像 (MRI) 上精确的膀癌 (BC) 分段对于评估肌肉入侵至关重要.
  • 多参数MRI (mp-MRI) 为BC检测提供了详细的解剖和功能信息.

研究的目的:

  • 为了评估三个深度学习 (DL) 模型的瘤细分性能:Unet,MAnet和PSPnet.
  • 在mp-MRI数据上比较不同损失函数 (交叉,子相似系数损失,焦点损失) 的有效性.

主要方法:

  • 膀瘤的细分是根据53名患者的T2加权 (T2WI),扩散加权成像/明显扩散系数 (DWI/ADC) 和对比增强的T1加权 (T1WI) 图像进行的.
  • 三个DL模型 (Unet,MAnet,PSPnet) 使用混合损失函数 (CE+DSC,FL) 进行训练.
  • 使用子相似系数 (DSC),豪斯多夫距离 (HD) 和预期校准误差 (ECE) 来评估性能.

主要成果:

  • 使用CE+DSC的MAnet在ADC,T2WI和T1WI图像中实现了最高的DSC值.
  • 使用CE+DSC的PSPnet在ADC,T2WI和T1WI上产生了最小的HD值.
  • 与T2WI相比,ADC和T1WI的细分精度优越;FL的PSPnet在ADC上显示了最低的ECE,而CE+DSC的MAnet在T2WI和T1WI上显示了最低的ECE.

结论:

  • 与Unet相比,MAnet和PSPnet,特别是具有混合CE+DSC损失功能,在膀癌细分方面表现优越,与Unet相比.
  • 评估指标的选择会影响mp-MRI上BC细分的最佳DL模型和损失函数.