Automated detection of tumor regions from oral histological whole slide images using fully convolutional neural networks

The diagnosis of different types of cancer, including oral cavity-derived cancer, is made by a pathologist through complex and time-consuming microscopic analysis of tissue samples. This paper presents a method based on a fully convolutional neural network to localize and perform refined segmentation of oral cavity-derived tumor regions in H&E-stained histological whole slide images. The proposed method uses color features in the HSV color model to identify tissue regions in a pre-processing step to remove background and nonrelevant areas. The identified tissue regions are then transformed into the CIE L*a*b* color model and split into image-patches. The method was applied in a WSI dataset of oral squamous cell carcinoma tissue samples. In addition, for further validation and comparison with other proposals, we also applied the proposed method in a WSI dataset of sentinel lymph nodes with breast cancer metastases. Experimental evaluations were performed using a total of 85,621 image-patches of size 640 x 640 pixels and the proposed method achieved good results in different cancer-derived datasets with images of different tumors. The results revealed that the proposal is robust and capable to localize and perform refined segmentation, achieving accuracy results up to 97.6%, specificity up to 98.4%, and sensitivity up to 92.9%. The influence of different color spaces and different image-patch sizes in the proposed method also were explored.

Automated summary

The study proposes a method based on a fully convolutional neural network for localizing and performing refined segmentation of tumor regions in histological whole slide images. Experimental results show that the method achieved good results in different cancer-derived datasets with high accuracy up to 97.6%.