An end-to-end brain tumor segmentation system using multi-inception-UNET

Accurate detection and pixel-wise classification of brain tumors in Magnetic Resonance Imaging (MRI) scans are vital for their diagnosis, prognosis study and treatment planning. Manual segmentation of tumors from MRI is highly subjective and tedious. With recent advances in deep learning, automatic brain tumor segmentation is an emerging research direction in the medical imaging domain. We present a study to improve the automatic segmentation process by introducing size variability in the Convolutional Neural Network (CNN). For pixel-wise classification of tumorous slices convolutional neural network-based encoder-decoder UNET model is referred. A multi-inception-UNET model is proposed to improve scalability of the UNET model. Extensive experiments have been performed using the Brain Tumor Segmentation Challenge (BRATS) datasets to establish the validity of our proposed model. Experimental results show that our proposed method achieved the best results on BraTS 2015, 2017 and 2019 datasets for complete tumor, core tumor and enhancing tumor regions respectively.