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对图像处理技术进行比较分析,以提高MRI图像质量:使用3D U-Net架构进行3D重建和细分

Chee Chin Lim^1,2, Apple Ho Wei Ling¹, Yen Fook Chong²

¹Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia.

Diagnostics (Basel, Switzerland)

|July 29, 2023

概括

一个新的卷积神经网络 (CNN) 在MRI扫描中自动细分骨髓瘤,提高了手工方法的效率. 这种深度学习方法对儿科骨癌检测有前途.

科学领域:

在瘤学瘤学.
医疗成像医学成像
人工智能的人工智能

背景情况:

骨髓瘤是儿童和青少年常见的骨瘤.
在MRI中手动细分骨髓瘤是耗时和主观的.
自动化细分可以提高诊断效率和可重复性.

研究的目的:

开发和评估一个卷积神经网络 (CNN) 用于MRI中自动骨髓瘤细分.
为了比较CNN在不同MRI序列 (T1W,T2W,T1W+Gd) 的性能.

主要方法:

获得了46名患者的3692张DICOMMRI图像.
应用对比度拉伸和中间选用于图像预处理.
利用3D U-Net架构进行基于深度学习的细分.

主要成果:

实现了高细分精度,平均子相似系数 (DSC) 为83.75% (T1W),85.45% (T2W) 和87.62% (T1W + Gd).
证明了3D U-Net模型在各种MRI模式下对骨髓瘤进行细分的潜力.

结论:

开发的基于CNN的方法提供了一种自动化的方法,用于MRI中的骨髓瘤细分.
局限性,如边界定义不佳和遗漏病变等,需要进一步精细化,以便在临床应用.

关键词:

3D U-Net 是一个 3D U-Net.这就是为什么MRI是MRI.骨癌细胞是一种癌细胞.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.骨质肉瘤是骨质肉瘤的一种疾病.瘤细分瘤的细分

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