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通过SAM模型中的类别描述符增强核细分和分类

Miguel Luna¹, Philip Chikontwe¹, Sang Hyun Park^1,2

¹Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of Korea.

Bioengineering (Basel, Switzerland)

|March 27, 2024

概括

这项研究使用SegmentAnything Model (SAM) 增强了在组织病理学图像中的细胞核细分和分类. 它通过对齐图像特征来改善罕见核类型的检测,提高F1得分高达12%.

科学领域:

数字病理学数字病理学
计算生物学计算生物学
医学图像分析医学图像分析

背景情况:

细胞核细分和分类在H&E基因病理学图像中面临的挑战是由于细胞类型的长尾分布.
图像细分的基础模型,如细分任何模型 (SAM),显示出改善罕见核的检测的希望.

研究的目的:

适应细分任何模型 (SAM) 以准确地对细胞核进行细分和分类.
解决自然场景图像和组织病理学图像之间的领域差距,以改善模型概括.
为了提高罕见核类型的检测.

主要方法:

使用类别描述符来提示SAM模型进行核细分和分类.
在低级空间中实现了特征对齐,以弥合域间隙,同时保留SAM的高级表示.
验证了类数据集的方法.

主要成果:

实现了原子核的自动细分和分类,特别是改善了罕见原子核类型的检测.
在罕见核检测方面,F1得分明显提高了12%.
保持与手动点提示的兼容性,以便在没有额外培训的情况下进行交互式改进.

结论:

拟议的方法有效地利用SAM进行细胞核分析.
这种方法成功地提高了罕见细胞类型的识别,这对于准确的诊断至关重要.
该模型为数字病理学应用提供了灵活和高效的工具.

关键词:

域名对齐域名对齐长尾分布分布的长尾动物核的分类核的分类.核的细分核的细分快速指导细分即时指导的细分

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