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基于深度学习的自动肝脏细分使用计算机断层扫描图像在狗

Seungyeon Lee¹, Genya Shimbo², Nozomu Yokoyama¹

¹Laboratory of Veterinary Internal Medicine, Department of Veterinary Clinical Science, Graduate School of Veterinary Medicine, Hokkaido University, Sapporo, Japan.

Frontiers in veterinary science

|November 6, 2025

概括

一个新的深度学习模型在CT扫描中准确地细分狗肝,改善兽医诊断. 这种自动化方法与手工测量具有很高的一致性,具有临床潜力.

科学领域:

兽医辐射学兽医辐射学
医学成像分析分析医学成像分析
医学中的人工智能.

背景情况:

使用深度学习 (DL) 的自动细分已经推进了人类医学.
狗肝在兽医领域的细分仍然是一个挑战.
准确的肝脏细分对于诊断和治疗狗肝脏疾病至关重要.

研究的目的:

开发和验证用于自动化犬肝细分的DL模型.
使用3D U-Net架构进行精确的细分.
评估模型在狗腹部CT扫描上的表现.

主要方法:

使用了221个狗腹部CT扫描的数据集.
3D U-Net 模型在两个不同的数据集上进行了训练和评估.
实验1:没有肝脏质量的病例. 实验2:有和没有肝脏质量的综合病例.

主要成果:

这两项实验都实现了高细分性能.
平均子相似系数为0.926 (实验1) 和0.929 (实验2).
在手动和预测的肝脏体积之间观察到出色的一致性.

结论:

开发的3D U-Net模型在狗CT扫描中提供了准确的自动化肝脏细分.
这种方法显示出在兽医中临床应用的巨大潜力.
进一步验证可以提高对犬肝病的诊断能力.

关键词:

人工智能的人工智能是人工智能.自动细分自动细分自动细分狗类犬类动物狗类犬类计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习是一种深度学习.狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗肝脏肝脏肝脏肝脏肝脏肝脏

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