Reversible Feature Learning for Brain Tumor Segmentation With Incomplete Modalities

Accurate brain tumor segmentation is vital for clinical diagnosis and treatment. Due to motion artifacts and image damage, it is challenging to obtain accurate segmentation results of brain tumors in the presence of incomplete MRI modalities. We propose a multimodal reversible feature learning method to tackle this problem. This method can fully explore the potential feature similarities and complementarities between MRI modalities. To compensate the information of missing modalities, we propose a reversible feature interaction module. It explores information similarity among existing modalities as priors, which are used to reconstruct features of missing modalities at the feature level. With enhanced discriminative information, the model suppresses the noise in the missing modalities. Furthermore, we propose a dual-scale attention module to enhance the detail restoration and reconstruction accuracy of images. Comparison results on the BRATS challenge datasets show the superiority of our method over current popular methods. The code used in this study is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/fybgogogo/reverse</uri>.