Multi‐level dilated convolutional neural network for brain tumour segmentation and multi‐view‐based radiomics for overall survival prediction. Issue 3 (28th January 2021)

• Record Type: Journal Article
• Authors: Rafi, Asra; Madni, Tahir Mustafa; Janjua, Uzair Iqbal; Ali, Muhammad Junaid; Abid, Muhammad Naeem
• Is Part Of: International journal of imaging systems and technology. Volume 31:Issue 3(2021)
• Page Start: 1519
• Page End: 1535
• Publication Date: 2021-01-28
• Subjects: brain disease -- brain tumour segmentation -- dilated convolution -- magnetic resonance imaging -- multi‐view -- overall survival -- random forest; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22549
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Glioblastoma (GBM) is the most high‐risk and grievous tumour in the brain that causes the death of more than 50% of the patients within one to 2 years after diagnosis. Accurate detection and prognosis of this disease are critical to provide essential guidelines for treatment planning. This study proposed using a deep learning‐based network for the GBM segmentation and radiomic features for the patient's overall survival (OS) time prediction. The segmentation model used in this study was a modified U‐Net‐based deep 3D multi‐level dilated convolutional neural network. It uses multiple kernels of altered sizes to capture contextual information at different levels. The proposed scheme for OS time prediction overcomes the problem of information loss caused by the derivation of features in a single view due to the variation in the neighbouring pixels of the tumorous region. The selected features were based on texture, shape, and volume. These features were computed from the segmented components of tumour in axial, coronal, and sagittal views of magnetic resonance imaging slices. The proposed models were trained and evaluated on the BraTS 2019 dataset. Experimental results of OS time prediction on the validation data have showed an accuracy of 48.3%, with the mean squared error of 92 599.598. On the validation data, the segmentation model achieved a mean dice similarity coefficient of 0.75, 0.89, and 0.80 for enhancing tumour, whole tumour, and tumour core, respectively. Future work is warranted to improve the overall performance of OS time prediction based on the findings in this study.