Global context and boundary structure-guided network for cross-modal organ segmentation

In multi-modal medical images such as X-ray and CT, automated organ segmentation is often disturbed by the frequent existence of shape variation, intensity non-uniformities and blurs. Introducing supplementary information helps with eliminating the negative effects of these factors. Medical priors are often leveraged to supplement for specific tasks in previous works, but the specificity of priors hinders these methods from generalizing to cross-modal problems. In this paper we propose Global Context and Boundary Structure-guided Network (GCBSN), which utilizes global context and boundary structure to assist cross-modal organ segmentation. Specifically, we innovatively employ the global context from all spatial regions to guide our deformable convolution. Therefore, more suitable receptive fields can be generated for irregular-shaped targets. Also, we extract the global context contained in each class from the coarse segmentation to assist classifying the areas with non-uniform intensity. Moreover, we design a novel loss that weights more on the errors of positions nearby organ boundaries, and this loss function can avoid errors brought by border blurs. The cross-modal performance of GCBSN is evaluated on two datasets of different modals, i.e., X-Ray images and CT slices. On the 3D NIH Pancreas Dataset, GCBSN outperforms the baseline by 8.72% in terms of the dice coefficient (DC). On the 2D Japanese Society of Radiological Technology Dataset, the mean DC of GCBSN is 98.07% for lung and 94.91% for heart and it surpasses other state-of-the-art methods.