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一个多对象深度神经网络架构来检测前列腺解剖在T2加权MRI:性能评估评估

Maria Baldeon-Calisto¹, Zhouping Wei², Shatha Abudalou^2,3

¹Departamento de Ingeniería Industrial and Instituto de Innovación en Productividad y Logística CATENA-USFQ, Universidad San Francisco de Quito, Quito, Ecuador.

Frontiers in nuclear medicine

|October 9, 2024

概括

这项研究介绍了PPZ-SegNet,这是一种深度学习模型,用于在MRI扫描中对前列腺和外围区域进行细分. 该模型显示了有希望的结果,强调了需要多样化的网络来改善不同前列腺大小的细分.

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:

前列腺细分对于估计腺体体积和管理前列腺疾病至关重要.
精确的细分有助于诊断,治疗计划和前列腺疾病监测.

研究的目的:

开发和评估2D-3D卷积神经网络 (CNN) 组合,PPZ-SegNet,用于前列腺和外围区域的自动细分.
用T2加权MRI序列对各种公共数据集评估模型的性能.

主要方法:

使用贝叶斯式超参数优化开发了一个2D-3D CNN合集 (PPZ-SegNet).
该模型在150个T2WMRI前列腺病例上进行了训练,并使用五倍交叉验证进行了验证.
在四个独立的测试队列 (共283例) 中,使用Dice相似系数和Hausdorff距离来评估性能.

主要成果:

PPZ-SegNet实现了0.86 (第1次测试),0.79 (第二次测试),0.81 (第三次测试) 和0.62 (第四次测试) 的平均子得分.
在四个测试队伍中,三组的前列腺体积较大时,细分性能有所改善.
跨队伍子得分的变化表明需要更普遍的模型.

结论:

开发的PPZ-SegNet在T2WMRI中证明了前列腺和外围区域的有效自动细分.
该研究强调了模型多样性的重要性,以适应腺体大小的变化和其他普遍细分的因素.
需要进一步开发,以创建一个强大的,通用网络,以进行全面的前列腺细分.

关键词:

通过AdaEn-Net进行调整.埃蒙纳斯 (EMONAS) 是一个国际经济组织.深度学习是一种深度学习.机器学习是机器学习.神经架构搜索神经架构搜索神经网络的神经网络的神经网络前列腺癌是前列腺癌.前列腺的细分前列腺的细分.

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