用于肿瘤生物标记物评分的全切片图像实时分割和分类

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Md Jahid Hasan , Wan Siti Halimatul Munirah Wan Ahmad , Mohammad Faizal Ahmad Fauzi , Jenny Tung Hiong Lee , See Yee Khor , Lai Meng Looi , Fazly Salleh Abas , Afzan Adam , Elaine Wan Ling Chan
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引用次数: 0

摘要

组织病理学图像分割和分类对于诊断和治疗乳腺癌至关重要。本研究采用单一架构对组织病理学图像进行高精度分割和分类。我们利用了著名的分割架构 SegNet 和 U-Net,并修改了解码器以附加 ResNet、VGG 和 DenseNet 来执行分类任务。这些混合模型与作为骨干的 Stardist 集成,并通过图形用户界面在病理学家实时工作流程中实施。使用 ER-IHC 染色私人数据集和 H&E 染色公共数据集 (MoNuSeg) 对这些模型进行了离线训练和测试。为了进行实时评估,使用 PR-IHC 染色玻璃切片对所提出的模型进行了评估。在私人数据集和公共数据集上,基于像素的分割 F1 分数分别为 0.902 和 0.903,在私人数据集上,基于分类的 F1 分数为 0.833。实验显示了我们方法的鲁棒性,在 ER-IHC 数据集上训练的模型能够在 20 倍和 40 倍放大率的 PR-IHC 切片实时显微镜检查中表现良好。这将有助于病理学家快速做出决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time segmentation and classification of whole-slide images for tumor biomarker scoring
Histopathology image segmentation and classification are essential for diagnosing and treating breast cancer. This study introduced a highly accurate segmentation and classification for histopathology images using a single architecture. We utilized the famous segmentation architectures, SegNet and U-Net, and modified the decoder to attach ResNet, VGG and DenseNet to perform classification tasks. These hybrid models are integrated with Stardist as the backbone, and implemented in a real-time pathologist workflow with a graphical user interface. These models were trained and tested offline using the ER-IHC-stained private and H&E-stained public datasets (MoNuSeg). For real-time evaluation, the proposed model was evaluated using PR-IHC-stained glass slides. It achieved the highest segmentation pixel-based F1-score of 0.902 and 0.903 for private and public datasets respectively, and a classification-based F1-score of 0.833 for private dataset. The experiment shows the robustness of our method where a model trained on ER-IHC dataset able to perform well on real-time microscopy of PR-IHC slides on both 20x and 40x magnification. This will help the pathologists with a quick decision-making process.
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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