SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities

• URL: http://arxiv.org/abs/2204.02961v1
• Date: Wed, 6 Apr 2022 17:55:19 GMT
• Title: SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
• Authors: Reza Azad, Nika Khosravi, Dorit Merhof
• Abstract summary: We propose a style matching U-Net (SMU-Net) for brain tumour segmentation on MRI images. Our co-training approach utilizes a content and style-matching mechanism to distill the informative features from the full-modality network into a missing modality network. Our style matching module adaptively recalibrates the representation space by learning a matching function to transfer the informative and textural features from a full-modality path into a missing-modality path.
• Score: 4.855689194518905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gliomas are one of the most prevalent types of primary brain tumours, accounting for more than 30\% of all cases and they develop from the glial stem or progenitor cells. In theory, the majority of brain tumours could well be identified exclusively by the use of Magnetic Resonance Imaging (MRI). Each MRI modality delivers distinct information on the soft tissue of the human brain and integrating all of them would provide comprehensive data for the accurate segmentation of the glioma, which is crucial for the patient's prognosis, diagnosis, and determining the best follow-up treatment. Unfortunately, MRI is prone to artifacts for a variety of reasons, which might result in missing one or more MRI modalities. Various strategies have been proposed over the years to synthesize the missing modality or compensate for the influence it has on automated segmentation models. However, these methods usually fail to model the underlying missing information. In this paper, we propose a style matching U-Net (SMU-Net) for brain tumour segmentation on MRI images. Our co-training approach utilizes a content and style-matching mechanism to distill the informative features from the full-modality network into a missing modality network. To do so, we encode both full-modality and missing-modality data into a latent space, then we decompose the representation space into a style and content representation. Our style matching module adaptively recalibrates the representation space by learning a matching function to transfer the informative and textural features from a full-modality path into a missing-modality path. Moreover, by modelling the mutual information, our content module surpasses the less informative features and re-calibrates the representation space based on discriminative semantic features. The evaluation process on the BraTS 2018 dataset shows a significant results.

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