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在跨腹部超声波图像中自动估计前列腺体积

Tiziano Natali¹, Liza M Kurucz², Matteo Fusaglia³

¹Image-Guided Surgery, Department of Surgical Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands; Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Postbus 217, 7500AE Enschede, the Netherlands.

European journal of radiology

|July 4, 2025

概括

这项研究引入了一个深度学习框架,用于估计前列腺体积 (PV) 使用非侵入性腹横超声波 (TAUS). 人工智能模型准确地测量PV,改善前列腺癌风险分层,而无需侵入性手术.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

前列腺癌需要准确的早期检测和风险分层方法.
前列腺体积 (PV) 对于风险评估至关重要,传统上通过跨直肠超声波 (TRUS) 侵入性测量.
腹腔超声波 (TAUS) 提供了一个非侵入性的替代方案,但其准确性受到图像质量和操作员依赖性的限制.

研究的目的:

开发和评估一个基于深度学习的框架,用于使用跨腹部超声波 (TAUS) 来自动估计前列腺体积.
通过精确的PV测量来改善非侵入性前列腺癌风险分层.

主要方法:

使用了100个跨腹部超声波 (TAUS) 视频的数据集,专家划分作为基本事实.
该框架采用了深度学习模型,用于以轴向和斜向平面进行前列腺细分,直径估计和随后的PV计算.
绩效是使用Dice分数,Hausdorff距离进行细分和体积误差进行体积估计来评估的.

主要成果:

深度学习框架实现了精确的前列腺体积估计,平均体积误差为-2.1mL.
轴向细分模型表现出卓越的性能,子得分为0.76 ± 0.16.
光伏估计的整体相对误差小于25%.

结论:

深度学习对准确和非侵入性前列腺体积估计具有重大潜力.
这种人工智能驱动的方法可以提高前列腺癌风险评估和分层.
开发的框架为改善患者舒适度和前列腺癌评估诊断准确度提供了一个有前途的工具.

关键词:

早期检测早期检测前列腺癌是什么意思前列腺癌是什么意思分段化分段化分段化分段化超声波成像超声波成像量估计量估计量估计量

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