Abstract
Automatic segmentation of brain tumors plays important role for diagnosis of cancer. The work explores CNN-based auto-segmentation of high-grade glioma using two models. Firstly, basic 3-dimensional VNet model is applied on 2D images using same architecture. Secondly, original WNet model is enhanced by making it deeper with additional convolutional layers at encoder-decoder paths of both UNet-like segments. Total 31 and 44 convolutional layers are used with 2D-VNet and modified WNet, respectively, to experiment on BraTS 2018 MRI data. It generated multi-region segmentation with three classes as per internal structures of tumor namely—enhancing, non-enhancing/necrosis, and edema. Test accuracy of 99.52%, 99.49%, dice scores of 0.9957, 0.9958, dice loss of 0.425, 0.414 are obtained by 2D-VNet and WNet, respectively. Training time taken by 2D-VNet and WNet is 44 and 77 s-per-epoch, respectively. Modified WNet exhibits more complexity than 2D-VNet model, whereas performance of both is almost similar.
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Gore, S., Bhosale, P., George, A., Mohan, A., Joshi, P., Thakare, A. (2022). Multimodal MRI Analysis for Segmentation of Intra-tumoral Regions of High-Grade Glioma Using VNet and WNet Based Deep Models. In: Nayak, J., Behera, H., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Data Mining. Smart Innovation, Systems and Technologies, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-9447-9_7
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