HR-ASPP: An improved semantic segmentation model of cervical nucleus images with accurate spatial localization and better shape feature extraction based on Deeplabv3+.

Cervical cancer, the fourth most common fatal cancer in women, has a considerably increased cure rate if identified and treated at early stages. Computer-aided diagnosis technology is the key for scaling up cervical cancer screening. Computer-processed images of nuclei can help doctors better analyze and diagnose the extent of cancer lesions. However, the lesion characteristics of cervical cancer are mainly reflected in the cell nuclei, which will be significantly larger, with distorted boundary shapes and deepened colors. What's worse is that the nuclei of diseased cells may be hidden in cell clusters, making it difficult for existing medical image segmentation models to achieve satisfactory results. In this paper, based on Deeplabv3+, we propose a HR-ASPP model with accurate spatial localization and better shape feature extraction to segment cervical nuclei. Firstly, HR-ASPP uses HRNet as the backbone network, which has good feature extraction ability for small targets. The structure of parallel multi-resolution and repeated multi-scale fusion makes the target information avoid losing in the deep network, so as to better extract the location information of cell nuclei. Secondly, we devise a lighter weight deformable convolution. The proposed HR-ASPP replaces part of the convolution in HRNet with a lighter weight deformable convolution to learn the edge shape of aberrant nuclei better while reducing the network computation. In addition, HR-ASPP incorporate CARAFE, which is able to aggregate information within a large receptive field and dynamically adapt to the content of a particular instance. The HR-ASPP method proposed in this paper achieves about 50.64%, 84.98% intersection over union (IoU) respectively on our dataset and the public datasets-ISBI. The results indicate that the proposed method achieves the best performance when compared with state-of-the-art approaches for pathologic image segmentation.