Abstract
Deep learning has been widely used to segment tumour regions in stained histopathology images. However, precise annotations are expensive and labour-consuming. To reduce the manual annotation workload, we propose a light annotation-based fine-level segmentation approach for histology images based on a VGG-based Fusion network with Global Normalisation CAM. The experts are only required to provide a rough segmentation annotation on the images, and then accurate fine-level segmentation boundaries can be produced using this method. To validate the proposed approach, three datasets with rough and fine quality segmentation annotation are built. The fine quality labels are used only as ground truth in evaluation. The VFGN-CAM method includes three main components: an annotation enhancement to boost boundary accuracy and model generalisability; a VGG Fusion module that integrates multi-scale tumour features; and a Global Normalisation CAM module that combines local and global gradient information of tumour regions. Our VGG fusion and Global Normalisation CAM outperform the existing methods with a Dice of 84.188%. The final improvement for our proposed methods against the original rough labels is around 22.8%.
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This work was supported by the Zhejiang Provincial Natural Science Foundation of China (No. LY21F020017, 2022C03043), National Natural Science Foundation of China (No. 61702146).
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Li, Y. et al. (2022). Light Annotation Fine Segmentation: Histology Image Segmentation Based on VGG Fusion with Global Normalisation CAM. In: Qin, W., Zaki, N., Zhang, F., Wu, J., Yang, F. (eds) Computational Mathematics Modeling in Cancer Analysis. CMMCA 2022. Lecture Notes in Computer Science, vol 13574. Springer, Cham. https://doi.org/10.1007/978-3-031-17266-3_12
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