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 in-house 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.