Multi-level feature fusion network for nuclei segmentation in digital histopathological images

Early detection and the classification of cancer in diagnosed patients can improve the prognosis and improve patient outcomes. In the field of histopathology, the assessment of the disease state is based on the morphological characteristics and spatial distribution of the nuclei in the tissue images. Therefore, the purpose of this research is to propose an automatic histopathological images nuclei segmentation method to accurately predict the boundaries of overlapping and multi-size nuclei. Based on the traditional U-Net, we proposed a convolutional neural network (CNN) that includes iterative attention feature fusion (iAFF) and residual modules for overlapping and multi-size nuclei segmentation task, which is essential and challenging for the development of computer-aided diagnosis (CAD) systems. We extensively evaluate this method on the TNBC and TCGA datasets and the experimental results show that our method can obtain better segmentation performance than the most advanced deep learning models. The proposed method has three advantages in the task of nuclei segmentation. First of all, the iAFF module used in the skip connection fully combines the global channel and the local context and overcomes the semantic and scale inconsistency between the input features. Second, the residual module in the decoder further integrates context information. Third, the method proposed in this paper will not increase too much computational overhead on U-Net, but the effect is significantly improved. Therefore, compared with traditional CNN, multi-level feature fusion network (MFFNet) can reduce redundancy and effectively improve the performance of the model without greatly increasing the network parameters.