bibtext: @InProceedings{ Zen_Missing_MICCAI2024,
                   author = { Zeng, Zhilin and Peng, Zelin and Yang, Xiaokang and Shen, Wei },
                   title = { { Missing as Masking: Arbitrary Cross-modal Feature Reconstruction for Incomplete Multimodal Brain Tumor Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: Automatic brain tumor segmentation using multimodal MRI images is a critical task in medical imaging. A complete set of multimodal MRI images for a subject offers comprehensive views of brain tumors, and thus providing ideal tumor segmentation performance. However, acquiring such modality-complete data for every subject is frequently impractical in clinical practice, which requires a segmentation model to be able to 1) flexibly leverage both modality-complete and modality-incomplete data for model training, and 2) prevent significant performance degradation in inference if certain modalities are missing. To meet these two demands, in this paper, we propose M$^3$FeCon (\textbf{M}issing as \textbf{M}asking: arbitrary cross-\textbf{M}odal \textbf{Fe}ature Re\textbf{Con}struction) for incomplete multimodal brain tumor segmentation, which can learn approximate modality-complete feature representations from modality-incomplete data. Specifically, we treat missing modalities also as masked modalities, and employ a strategy similar to Masked Autoencoder (MAE) to learn feature-to-feature reconstruction across arbitrary modality combinations. The reconstructed features for missing modalities act as supplements to form approximate modality-complete feature representations. Extensive evaluations on the BraTS18 dataset demonstrate that our method achieves state-of-the-art performance in brain tumor segmentation with incomplete modalities, especicall in enhancing tumor with 4.61\% improvement in terms of Dice score.
title: Missing as Masking: Arbitrary Cross-modal Feature Reconstruction for Incomplete Multimodal Brain Tumor Segmentation
authors: Zeng, Zhilin Peng, Zelin Yang, Xiaokang Shen, Wei