Light Annotation Fine Segmentation: Histology Image Segmentation Based on VGG Fusion with Global Normalisation CAM

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 histopathology 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%. The codes are released at:xxx.

Sketch-Supervised Histopathology Tumour Segmentation: Dual CNN-Transformer With Global Normalised CAM

“…for sketch-based tumour segmentation tasks. Compared with the recent VF-based method (VF+CAM) [47], our proposed method outperforms 14.55 % of Recall, 14.04 % of IOU and 10.76 % of Dice, which is a significant improvement on the BSS dataset.…”

“…The BSS dataset [47] is a private tumour dataset and has been adopted in our previous work. The BSS dataset contains 150 WSIs of squamous cell carcinoma including basal cell cancer (BCC), squamous papilloma (SP) and seborrheic keratosis cancer (SKC).…”