Abstract
An important stage in the diagnosis and treatment of brain cancer is the segmentation of the brain tumor using multimodal magnetic resonance imaging (MRI), but its normal to miss some modalities in daily clinical practice. Dealing with missing modalities is a challenge in medical imaging. The missing MRI sequences ought to compensated because mixture of a range of predetermined modes chosen primarily with circumstance & anatomical portion undergoing scanning will give medical personnel with comprehensive details on the targeted area in the human body. Since there is a strong relation between modalities, multi-modal MRI significantly add to the accuracy of brain tumor segmentation. This literature study examines several networks that attempt to reduce the negative effects of this problem using various strategies that have been developed over time. Techniques that use deep learning, including mutual information maximization, knowledge distillation networks, and common latent space models. This paper discusses different networks that solve the missing modalities problem. It also discusses about datasets, evaluation metrices, experimental analysis and accuracy.
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Shah, D., Barve, A., Vala, B., Gandhi, J. (2023). A Survey on Brain Tumor Segmentation with Missing MRI Modalities. In: Murthy, B.K., Reddy, B.V.R., Hasteer, N., Van Belle, JP. (eds) Decision Intelligence. InCITe 2023. Lecture Notes in Electrical Engineering, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-99-5997-6_26
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DOI: https://doi.org/10.1007/978-981-99-5997-6_26
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