FBUNet: Full Convolutional Network Based on Fusion Block Architecture for Biomedical Image Segmentation

Abstract

Precise segmentation of organs or tumors is essential for diagnosis and prognosis. We propose two novel improved end-to-end segmentation models, i.e. FBUNet-1 and FBUNet-2. The FBUNet-1 model shows higher performance by reducing the loss of spatial information in convolutional operations than the classic U-Net. The FBUNet-2 model can further increase accuracy by modifying the loss function based on the FBUNet-1 model. In this research, we compare the proposed models with the classic U-Net and deep residual U-Net models against four evaluation indexes, i.e. Dice coefficient, Jaccard similarity and Sensitivity and Precision respectively. The experimental results show that with a cut of almost one-third of the training parameters, the FBUNet-1 and FBUNet-2 models can still improve comprehensive performance in the cell edge segmentation, blood vessel segmentation, lung segmentation and cell nuclei segmentation. For example, the average Dice coefficient is 93.96%, Jaccard Similarity 88.62%, Sensitivity 94.19% and Precision 93.73% in cell segmentation. In addition, the average of fivefold cross validation of the proposed FBUNet-2 model increases by 0.5% of Jaccard Similarity, 0.3% of Dice coefficient and 0.9% of Jaccard Similarity, 0.6% of Dice coefficient for cell edge segmentation and cell nuclei segmentation compare with U-Net model. Compared with deep residual U-Net and classic U-Net models, the FBUNet-1 and FBUNet-2 models have potential and practical clinical applications.