图莫合成获取对人工智能的乳腺轮和脂肪/密度组织3D细分的影响:基于模拟的研究
Bruno Barufaldi1, Jordy Gomes2, Thais G do Rego2
1Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Tomography (Ann Arbor, Mich.)
|July 25, 2023
概括
通过使用一种新的虚拟临床试验方法,改善了数字乳腺图莫合成 (DBT) 细分. 这种方法增强了乳腺组织和轮细分,克服了传统扫描方法的文物.
科学领域:
- 医疗成像医学成像
- 放射学中的人工智能
- 图像细分 图像细分
背景情况:
- 数字乳腺断层合成 (DBT) 重建呈现出像外平面信号和不准确的组织边界这样的工件.
- 这些文物显著挑战了密度/脂肪乳腺组织的准确细分以及整体乳腺轮 (凸的船体).
研究的目的:
- 开发和评估一种虚拟临床试验方法,用于在DBT重建中对乳腺组织和乳腺轮进行细分.
- 评估不同采集几何形状和细分损失函数对细分精度的影响.
主要方法:
- 从代表性人口中模拟DBT图像,使用三个采集几何:常规 (I),T形双向 (II) 和超宽范围 (XWR,III).
- 修改了nnU-Net深度学习模型,其中有两个细分损失:一个用于组织,一个用于乳房轮.
- 评估了单个和组合损失函数的影响,并比较了不同采购几何体的细分性能.
主要成果:
- 结合损失函数 (组织 + 轮) 改善了凸船体的估计,将虚假空气 voxel 分类降低了 22.2%.
- 几何学II ("T"形) 在解决虚假空气voxel分类方面显著优于几何学I和III (分别为99.1%和96.8%).
- 几何学III (XWR) 与几何学I (0.92,0.78) 和II (0.93,0.74) 相比,实现了优越的组织细分 (脂肪组织的迪斯得分为0.98和密度组织的0.94).
结论:
- 结合的细分损失功能提高了乳房轮和组织细分精度在DBT.
- 与传统扫描相比",T"形和XWR采集几何形状在细分精度上提供了显著的改进.
- 这种虚拟试验方法为优化DBT采集和重建提供了一个强大的框架,以提高诊断性能.
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