基于数字病理学图像的颜色恢复.
Guoxin Sun1, Xiong Yan2, Huizhe Wang1
1School of Clinical Medicine, Qingdao University, Qingdao, China.
PloS one
|June 28, 2023
概括
这项研究表明,色彩转移算法有效地恢复了色的数字病理图像. 增强图像满足诊断需求,并改善深度学习模型的细胞识别.
科学领域:
- 数字病理学数字病理学
- 图像处理 图像处理
- 计算生物学 计算生物学
背景情况:
- 由于暴露在光线下,血素和 (HE) 染色病理学幻灯片的色可能会损害诊断的准确性.
- 病理学幻灯片的数字存档需要强大的方法来保持图像的完整性.
- 现有的图像增强技术可能无法充分恢复诊断特定的颜色信息.
研究的目的:
- 开发和评估一种用于恢复色的数字病理图像的色彩传输算法.
- 评估色彩恢复对图像质量指标和诊断实用性的影响.
- 为了确定恢复图像的有效性,以便以后基于深度学习的细胞识别.
主要方法:
- HE染色的侵袭性乳腺癌组织样本经过8周的模拟色.
- 应用了一种色彩转移算法,以数字恢复色图像的颜色.
- 使用自然图像质量评估器 (NIQE),信息 (Entropy) 和平均梯度 (AG) 来定量评估图像质量.
- 使用UNet++模型评估恢复图像上的细胞识别率.
主要成果:
- 色彩恢复显著改善了图像质量,由NIQE (P<0.05) 的降低和入 (P<0.01) 和平均梯度 (P<0.01) 的增加值证明.
- 恢复后的图像成功地恢复了细胞核和细胞质之间的色彩对比,满足了病理学家的诊断要求.
- 使用UNet++模型的细胞识别率在彩色恢复图像上显著提高.
结论:
- 颜色转移算法为修复色的数字病理图像提供了有效的解决方案.
- 恢复后的图像显示出更好的诊断质量和更高的自动细胞识别性能.
- 这种方法有可能保留数字病理档案的诊断价值.
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