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在全场数字乳房显微镜中进行乳房划线,使用分段任何模型.

Andrés Larroza¹, Francisco Javier Pérez-Benito¹, Raquel Tendero¹

¹Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain.

Diagnostics (Basel, Switzerland)

|May 24, 2024

概括

SAM-乳房模型在乳房影像中准确地对乳房区域进行细分,改善了计算机辅助诊断. 这种先进的细分方法增强了乳房划线和胸部肌肉排除,以更好地检测癌症.

关键词:

乳房细分乳房细分乳房学乳房学乳房学分段任何东西模型 (SAM)

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科学领域:

医疗成像医学成像
医疗保健中的人工智能
放射学放射学是一门学科.

背景情况:

乳腺扫描对于乳腺癌查至关重要,但面临着低对比度,噪音和文物等挑战.
精确的乳房细分对于乳房摄影中有效的计算机辅助诊断 (CAD) 系统至关重要.
现有的方法在精确的乳房划线和乳房肌肉排除方面扎,在各种乳房影像视图中.

研究的目的:

引入和评估SAM-乳房模型用于乳房显微镜中的自动化乳房细分.
为了提高乳房区域划分和胸部肌肉排除的准确性.
评估模型在不同数据集和乳腺造影视图 (MLO和CC) 中的性能.

主要方法:

将分段任何模型 (SAM) 调整为用于乳房影像细分的SAM-乳房模型.
在一个大型的,多中心的专有数据集上训练模型,包括2492张乳房影像.
使用来自五个数据集 (两个专有,三个公共) 的独立测试图像验证了性能.

主要成果:

实现了99.22%±1.13的高整体子相似系数 (DSC) 和98.48%±2.10的交叉在联盟 (IoU) 的交叉.
在不同的数据集,供应商和图像分辨率上展示了一致的性能.
超出基线性能和其他基于深度学习的细分方法.

结论:

SAM-乳房模型有效地对乳房区域进行乳房影像细分,展示了SAM的适应性.
该方法提供了强大的,灵活的和可通用的乳腺细分能力.
这一进步对改善乳腺癌查中的计算机辅助诊断具有重大潜力.