College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580 Shandong, China.
College of Computer and Information Science, Inner Mongolia Agricultural University, Huhhot, 010018 Inner Mongolia, China.
Comput Math Methods Med. 2021 Jul 1;2021:4244157. doi: 10.1155/2021/4244157. eCollection 2021.
Histological analysis to tissue samples is elemental for diagnosing the risk and severity of ovarian cancer. The commonly used Hematoxylin and Eosin (H&E) staining method involves complex steps and strict requirements, which would seriously impact the research of histological analysis of the ovarian cancer. Virtual histological staining by the Generative Adversarial Network (GAN) provides a feasible way for these problems, yet it is still a challenge of using deep learning technology since the amounts of data available are quite limited for training. Based on the idea of GAN, we propose a weakly supervised learning method to generate autofluorescence images of unstained ovarian tissue sections corresponding to H&E staining sections of ovarian tissue. Using the above method, we constructed the supervision conditions for the virtual staining process, which makes the image quality synthesized in the subsequent virtual staining stage more perfect. Through the doctors' evaluation of our results, the accuracy of ovarian cancer unstained fluorescence image generated by our method reached 93%. At the same time, we evaluated the image quality of the generated images, where the FID reached 175.969, the IS score reached 1.311, and the MS reached 0.717. Based on the image-to-image translation method, we use the data set constructed in the previous step to implement a virtual staining method that is accurate to tissue cells. The accuracy of staining through the doctor's assessment reached 97%. At the same time, the accuracy of visual evaluation based on deep learning reached 95%.
对组织样本进行组织学分析对于诊断卵巢癌的风险和严重程度至关重要。常用的苏木精和伊红（H&E）染色方法涉及复杂的步骤和严格的要求，这将严重影响卵巢癌组织学分析的研究。基于生成对抗网络（GAN）的虚拟组织学染色为这些问题提供了一种可行的方法，但由于可用数据量非常有限，因此仍然是深度学习技术的挑战。基于 GAN 的思想，我们提出了一种弱监督学习方法，用于生成未染色的卵巢组织切片的自发荧光图像，这些图像对应于卵巢组织的 H&E 染色切片。使用上述方法，我们构建了虚拟染色过程的监督条件，这使得后续虚拟染色阶段合成的图像质量更加完美。通过医生对我们结果的评估，我们方法生成的卵巢癌未染色荧光图像的准确性达到了 93%。同时，我们评估了生成图像的图像质量，其中 FID 达到 175.969，IS 得分达到 1.311，MS 达到 0.717。基于图像到图像的翻译方法，我们使用上一步构建的数据集中实现了一种针对组织细胞的精确虚拟染色方法。医生评估的染色准确性达到了 97%。同时，基于深度学习的视觉评估准确性达到了 95%。
