CMFCUNet: cascaded multi-scale feature calibration UNet for pancreas segmentation

Segmenting the pancreas from abdominal CT scans is challenging since it often takes up a relatively small region. Researchers suggested leveraging coarse-to-fine approaches to cope with this challenge. However, the coarse-scaled segmentation and the fine-scaled segmentation are either trained separately utilizing the coordinates located by the coarse-scaled segmentation mask to crop the fine-scaled segmentation input, or trained jointly utilizing the coarse-scaled segmentation mask to enhance the fine-scaled segmentation input. We argued that these two solutions are complementary to some extent and can promote each other to improve the performance of pancreas segmentation. In addition, the backbone in the coarse-scaled segmentation and fine-scaled segmentation is mostly based on UNet or UNet-like networks, where the multi-scale features transmitted from the encoder to the decoder have not been explored for vertical calibration before. In this paper, we propose a cascaded multi-scale feature calibration UNet (CMFCUNet) for pancreas segmentation where the multi-scale features in the backbone of each scaled segmentation are calibrated vertically in a pixel-wise fashion. Besides, the coarse-scaled segmentation and the fine-scaled segmentation are connected by leveraging a designed dual enhancement module (DEM). Experiments are first conducted on the public NIH pancreas dataset. First, when leveraging CMFCUNet, our method increased by over 3% on the Jaccard index (JI) and nearly 1% on dice similarity coefficient (DSC) which surpasses all existing pancreas segmentation approaches. In addition, our experiments demonstrate that CMFCUNet improved the coarse-to-fine segmentation framework and outperformed the mainstream coarse-to-fine pancreas segmentation approaches. Furthermore, we also conducted ablation studies to analyze the effectiveness of the backbone (MFCUNet) and the DEM. In addition to the experiments on the NIH dataset, we also experimentally demonstrate the excellent generalization of our method on the MSD pancreas dataset.