(ISMRM 2024) Efficient Multi-modality MRI Fusion Based on Superpixel Method for Semi-supervised Brain Tumor Segmentation
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Abstract #2125

Efficient Multi-modality MRI Fusion Based on Superpixel Method for Semi-supervised Brain Tumor Segmentation

Yifan Deng1, Sa Xiao1, Zhen Chen1, Cheng Wang1, and Xin Zhou1
1State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan National Laboratory for Optoelectronics, Wuhan,China, China

Synopsis

Keywords: Segmentation, Brain

Motivation: The multi-modal images can provide complementary information to improve automatic MRI segmentation performance. However, most multi-modal methods require long training times due to the complex network structures and the large amounts of multi-modal images involved. Furthermore, obtaining numerous labeled data is time-consuming and laborious.

Goal(s): To achieve efficient and high-quality segmentation using only a few labeled data.

Approach: We propose an efficient training-free multi-modal fusion strategy based on superpixel method for semi-supervised brain tumor segmentation.

Results: The experiments on BraTS18 dataset show that our method results in superior overall performance, and can greatly reduce the time costs of doctors.

Impact: The strategy of using superpixel method to accelerate the network training process can assist in the timely diagnosis and treatment of diseases in the clinic, and provides a new idea to simplify multi-modal information fusion.

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Keywords

segmentationmodalfusionnetworkbraintumortrainingdiceefficientsemisupervisedmodulelabelslosschinalabeled