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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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一个边界增强的肝脏细分网络用于多相CT图像与无监督域调整.

Swathi Ananda¹, Rahul Kumar Jain¹, Yinhao Li¹

¹Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu-shi 525-0058, Japan.

Bioengineering (Basel, Switzerland)

|August 26, 2023

概括

此摘要是机器生成的。

这项研究引入了一种基于双分辨器的无监督域适应 (DD-UDA) 方法,用于在多相CT图像中准确地细分肝脏. 该方法克服了注释挑战和差异差异,大大提高了细分精度,而不需要多阶段注释.

关键词:

边界增强加强边界增强深度学习是一种深度学习.肝脏细分细分肝脏的细分多相CT图像多相CT图像无监督的域名适应

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科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

多相计算机断层扫描 (CT) 对于诊断肝脏疾病至关重要.
多相CT中的肝脏细分面临诸多挑战,包括广泛的注释要求和某些阶段的差异差异.
现有的方法在不同CT阶段的域转移中扎,需要多阶段的注释.

研究的目的:

为多相CT图像开发一种有效的肝脏细分方法,以解决注释负担和对比度问题.
提出基于双区分器的无监督域调整 (DD-UDA) 框架,以实现没有多阶段注释的细分.
通过改进低对比度CT图像的边界识别来提高细分精度.

主要方法:

提出了一种基于双重歧视者的无监督域适应 (DD-UDA) 网络,用于肝脏细分.
实施了功能级和输出级的区分器,以减少域分布差异.
引入了一个边界增强的解码器,以改善在具有挑战性的对比阶段识别肝脏边界.

主要成果:

与基线UDA和其他最先进的方法相比,DD-UDA方法在目标MPCT-FLLs数据集上取得了优异的肝脏细分性能.
在没有多阶段注释的情况下,在不同阶段 (PV,ART,NC) 的交叉与欧盟 (IoU) 分数中显著改善.
实现了0.823 (PV),0.811 (ART) 和0.800 (NC) 的IoU得分,相应地超过了基线的0.785,0.796和0.772.

结论:

拟议的DD-UDA方法有效地解决了多相CT图像中肝脏细分的挑战.
无监督的域名适应与边界增强相结合,显著提高了细分精度,减少了注释劳动.
该方法在使用多相CT扫描诊断肝病时具有很强的临床应用潜力.