Segmenting organs in CT scan images is a necessary process for multiple
downstream medical image analysis tasks. Currently, manual CT scan segmentation
by radiologists is prevalent, especially for organs like the pancreas, which
requires a high level of domain expertise for reliable segmentation due to
factors like small organ size, occlusion, and varying shapes. When resorting to
automated pancreas segmentation, these factors translate to limited reliable
labeled data to train effective segmentation models. Consequently, the
performance of contemporary pancreas segmentation models is still not within
acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet
and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that
operates in two stages to gradually segment pancreas CT images from coarse to
fine with mask guidance for object detection. This approach empowers the
network to surpass segmentation performance achieved by similar network
architectures and achieve results that are on par with complex state-of-the-art
methods, all while maintaining a low parameter count. Additionally, we
introduce external contour segmentation as a preprocessing step for the coarse
stage to assist in the segmentation process through image standardization. For
the fine segmentation stage, we found that applying a wavelet decomposition
filter to create multi-input images enhances pancreas segmentation performance.
We extensively evaluate our approach on the widely known NIH pancreas dataset
and MSD pancreas dataset. Our approach demonstrates a considerable performance
improvement, achieving an average Dice Similarity Coefficient (DSC) value of up
to 89.53% and an Intersection Over Union (IOU) score of up to 81.16 for the NIH
pancreas dataset, and 88.60% DSC and 79.90% IOU for the MSD Pancreas dataset.

提出了 M3BUNet，一种融合 MobileNet 和 U-Net 神经网络的方法，通过逐步分割胰腺 CT 图像从粗到细的层次，并结合对象检测引导的掩码，超越了类似网络结构的分割性能，并取得了与复杂最先进方法相媲美的结果，同时保持较低的参数数量。