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Brain Tumour Segmentation with a Muti-Pathway ResNet Based UNet


Automatic segmentation of brain tumour regions is essential in today’s scenario for proper diagnosis and treatment of the disease. Gliomas can appear in any region and can be of any shape and size, which makes automatic detection challenging. However, now, with the availability of high-quality MRI scans, various strides have been made in this field. In this paper, we propose a novel multi-pathway UNet incorporated with residual networks and skip connections to segment multimodal Magnetic Resonance images into three hierarchical glioma sub-regions. The multi-pathway serves as a medium to decompose the multiclass segmentation problem into subsequent binary segmentation tasks, where each pathway is responsible for segmenting one class from the background. Instead of a cascaded architecture for the hierarchical regions, we propose a shared encoder, followed by separate decoders for each category. Residual connections employed in the model facilitate increasing the performance. Experiments have been carried out on BraTS 2020 dataset and have achieved promising results.

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Correspondence to Suresh Chandra Satapathy.

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Saha, A., Zhang, YD. & Satapathy, S.C. Brain Tumour Segmentation with a Muti-Pathway ResNet Based UNet. J Grid Computing 19, 43 (2021).

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  • Glio3ma
  • Multimodal MR images
  • Multiclass segmentation
  • Encoder
  • Decoder