Cervical cancer is a public health problem, where the treatment has a better chance of success if detected early. The analysis is a manual process which is subject to a human error, so this paper provides a way to analyze argyrophilic nucleolar organizer regions (AgNOR) stained slide using deep learning approaches. Also, this paper compares models for instance and semantic detection approaches. Our results show that the semantic segmentation using U-Net with ResNet-18 or ResNet-34 as the backbone have similar results, and the best model shows an IoU for nucleus, cluster, and satellites of 0.83, 0.92, and 0.99 respectively. For instance segmentation, the Mask R-CNN using ResNet-50 performs better in the visual inspection and has a 0.61 of the IoU metric. We conclude that the instance segmentation and semantic segmentation models can be used in combination to make a cascade model able to select a nucleus and subsequently segment the nucleus and its respective nucleolar organizer regions (NORs).
翻译：子宫颈癌是一个公共健康问题, 治疗如果早期检测成功的可能性更大。 分析是一个人工过程, 可能会发生人为错误, 因此本文提供一种方法, 使用深层学习方法分析超光速嗜血核核子组织区域( Agnor) 的污损幻灯片。 此外, 本文比较了模型和语义检测方法。 我们的结果表明, 使用 U- Net 和 ResNet-18 或 ResNet-34作为主干线的语义分解效果相似, 最佳模型显示核心、 集群和卫星的IOU 分别为0.83、 0. 92 和 0.99。 例如, 使用 ResNet- 50 的遮罩 R- CNN 在视觉检查中表现得更好, 并拥有IOU 度的0.61。 我们的结论是, 例分解和语义分解模型可以结合使用, 使级联模型能够选择核心, 并随后选择核心及其各自的核核子组织区域( NORs ) 。
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