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一个多模态的盆腔MRI数据集,用于基于深度学习的盆腔器官细分在子宫内膜异位症

Xiaomin Liang¹, Linda A Alpuing Radilla^2,3, Kamand Khalaj^1,4

¹McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA.

Scientific data

|July 24, 2025

概括

这项研究为子宫内膜异位症研究引入了新的女性盆腔MRI数据集. 这些数据支持开发自动化细分工具,以改善对这种常见疾病的诊断和理解.

科学领域:

医疗成像医学成像
放射学放射学是一门学科.
盆腔解剖学盆腔解剖学

背景情况:

子宫内膜异位症影响全球1.9亿名女性,需要先进的诊断工具.
磁共振成像 (MRI) 是对子宫内膜异位症诊断的首选非侵入性方法.
在MRI中精确细分盆腔结构对于子宫内膜异位症的评估至关重要.

研究的目的:

为子宫内膜异位症研究提供新的多中心女性盆腔MRI数据集.
评估自细分管道 (nnU-Net 和 RAovSeg) 对于与子宫内膜异位相关的骨盆结构的基线性能.
提供一个公开可用的资源,以推进盆腔MRI自分割.

主要方法:

从两个临床机构收集了多序子宫内膜异位症MRI扫描.
创建了一个多中心数据集 (51名受试者) 与手册标签用于评价者间协议评估.
使用单中心数据集 (81名受试者) 开发了卵巢自分段管道.

主要成果:

公开可用的数据集包括子宫,卵巢和可检测的子宫内膜瘤细分.
在 nnU-Net 和 RAovSeg 自动细分管道的基础性能证明.
突出了手动卵巢细分方面的挑战,强调了对自动化解决方案的需求.

结论:

这些数据集对于子宫内膜异位症的研究和盆腔MRI自分区的开发非常有价值.
自动化细分方法对于提高子宫内膜异位症诊断的效率和准确性至关重要.
公共数据共享加速了对妇科疾病的医学成像创新的发展.

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