Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities

TL;DR

This paper introduces a novel brain tumor segmentation method that leverages a correlation model to effectively handle missing MRI modalities, improving robustness and accuracy in clinical scenarios.

Contribution

It proposes a correlation-based representation learning approach that enhances segmentation performance with incomplete multi-modal MRI data.

Findings

Outperforms state-of-the-art methods on BraTS datasets

Maintains high accuracy with missing modalities

Demonstrates robustness in clinical-like missing data scenarios

Abstract

Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and treatment planning. In addition, multi-modal MR images can provide complementary information for accurate brain tumor segmentation. However, it's common to miss some imaging modalities in clinical practice. In this paper, we present a novel brain tumor segmentation algorithm with missing modalities. Since it exists a strong correlation between multi-modalities, a correlation model is proposed to specially represent the latent multi-source correlation. Thanks to the obtained correlation representation, the segmentation becomes more robust in the case of missing modality. First, the individual representation produced by each encoder is used to estimate the modality independent parameter. Then, the correlation model transforms all the individual representations to the latent multi-source correlation representations. Finally, the correlation representations across modalities are fused via attention mechanism into a shared representation to emphasize the most important features for segmentation. We evaluate our model on BraTS 2018 and BraTS 2019 dataset, it outperforms the current state-of-the-art methods and produces robust results when one or more modalities are missing.