Instituto de Informática, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500, Porto Alegre, 91501-970, RS, Brazil.
Faculdade de Odontologia, Universidade Federal do Rio Grande do Sul, R. Ramiro Barcelos, 2492, Porto Alegre, 90035-003, RS, Brazil.
Comput Biol Med. 2024 Sep;180:108967. doi: 10.1016/j.compbiomed.2024.108967. Epub 2024 Aug 6.
Papanicolaou staining has been successfully used to assist early detection of cervix cancer for several decades. We postulate that this staining technique can also be used for assisting early detection of oral cancer, which is responsible for about 300,000 deaths every year. The rational for such claim includes two key observations: (i) nuclear atypia, i.e., changes in volume, shape, and staining properties of the cell nuclei can be linked to rapid cell proliferation and genetic instability; and (ii) Papanicolaou staining allows one to reliably segment cells' nuclei and cytoplasms. While Papanicolaou staining is an attractive tool due to its low cost, its interpretation requires a trained pathologist. Our goal is to automate the segmentation and classification of morphological features needed to evaluate the use of Papanicolaou staining for early detection of mouth cancer.
We built a convolutional neural network (CNN) for automatic segmentation and classification of cells in Papanicolaou-stained images. Our CNN was trained and evaluated on a new image dataset of cells from oral mucosa consisting of 1,563 Full HD images from 52 patients, annotated by specialists. The effectiveness of our model was evaluated against a group of experts. Its robustness was also demonstrated on five public datasets of cervical images captured with different microscopes and cameras, and having different resolutions, colors, background intensities, and noise levels.
Our CNN model achieved expert-level performance in a comparison with a group of three human experts on a set of 400 Papanicolaou-stained images of the oral mucosa from 20 patients. The results of this experiment exhibited high Interclass Correlation Coefficient (ICC) values. Despite being trained on images from the oral mucosa, it produced high-quality segmentation and plausible classification for five public datasets of cervical cells. Our Papanicolaou-stained image dataset is the most diverse publicly available image dataset for the oral mucosa in terms of number of patients.
Our solution provides the means for exploring the potential of Papanicolaou-staining as a powerful and inexpensive tool for early detection of oral cancer. We are currently using our system to detect suspicious cells and cell clusters in oral mucosa slide images. Our trained model, code, and dataset are available and can help practitioners and stimulate research in early oral cancer detection.
巴氏染色已成功用于协助宫颈癌的早期检测数十年。我们假设这种染色技术也可用于协助早期发现口腔癌，口腔癌每年导致约 30 万人死亡。提出这种说法的依据有两个关键观察结果：（i）核异型性，即细胞核的体积、形状和染色特性的改变可与细胞快速增殖和遗传不稳定性相关；以及（ii）巴氏染色可使人们可靠地分割细胞的细胞核和细胞质。尽管巴氏染色是一种具有成本效益的诱人工具，但它的解释需要经过训练的病理学家。我们的目标是使用于评估巴氏染色对早期发现口腔癌的用途的形态特征的分割和分类自动化。
我们构建了一个用于巴氏染色图像中细胞的自动分割和分类的卷积神经网络（CNN）。我们的 CNN 在一个由 52 名患者的 1563 张全高清图像组成的新的口腔黏膜细胞图像数据集上进行了训练和评估，该数据集由专家进行了注释。我们将模型的有效性与一组专家进行了比较。我们还在五个具有不同显微镜和相机、不同分辨率、颜色、背景强度和噪声水平的公共宫颈图像数据集上展示了模型的鲁棒性。
在与一组三名人类专家对来自 20 名患者的 400 张口腔黏膜巴氏染色图像的比较中，我们的 CNN 模型达到了专家级的性能。该实验的结果表现出高的组内相关系数（ICC）值。尽管是在口腔黏膜图像上进行训练，但它为五个公共的宫颈细胞数据集生成了高质量的分割和合理的分类。我们的巴氏染色图像数据集是在患者数量方面最具多样性的公开的口腔黏膜图像数据集。
我们的解决方案为探索巴氏染色作为一种强大而廉价的口腔癌早期检测工具的潜力提供了手段。我们目前正在使用我们的系统来检测口腔黏膜载玻片图像中的可疑细胞和细胞簇。我们提供经过训练的模型、代码和数据集，以帮助从业人员并激发早期口腔癌检测方面的研究。
