MRI image segmentation using machine learning networks and level set approaches

Abstract

<span>The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones.</span>

Distributed brain tumor diagnosis using a federated learning environment

“…A weight-sharing approach is applied to minimize the number of parameters in the convolutional layers of CNN [33], [34]. CNN is composed of three main components: to learn the temporal and spatial features of an image, a convolutional layer is used, followed by a subsampling (max-pooling) layer, and finally, a fully connected (FC) layer for classification [34], [35]. The central architecture of CNN is shown in Figure 3.…”