REDNet: Reliable Evidential Discounting Network for Multi-Modality Medical Image Segmentation

In the field of computer-aided diagnosis, particularly for tumor diseases, segmentation is a prerequisite and primary step. Multi-modality images become essential for achieving accurate segmentation, which offer critical insights beyond the limitations of single-modality data. However, different modalities and images may suffer from different types of data imperfection, such as intensity non-uniformity, motion artifact, and low quality due to hardware limitations, which challenge image segmentation algorithms. To address this challenge, we propose a Reliable Evidential Discounting Network (REDNet), which is composed of three main modules: (1) the Intra-modality Consistency Evaluation Module (ICEM) measuring the data cohesion within the same modality; (2) the Cross-modality Difference Aggregation Module (CDAM) identifing data discrepancy across modalities; (3) the Discounting Fusion Module (DFM) processing the multi-modality evidence by applying discounting strategies to fuse the data. This approach maintains segmentation accuracy by effectively integrating multi-modality evidence, while discounting the influence of lower-quality data, ensuring reliable results despite the presence of image imperfections. We evaluated REDNet on two distinct datasets, BRATS2021 and an inhouse pancreas dataset from Changhai Hospital. REDNet outperforms other methods, particularly in scenarios with imperfect image sources, and achieves reliable results in multi-modality tumor segmentation.