A deep dense residual network with reduced parameters for volumetric brain tissue segmentation from MR images. (September 2021)

• Record Type: Journal Article
• Authors: Basnet, Ramesh; Ahmad, M. Omair; Swamy, M.N.S.
• Is Part Of: Biomedical signal processing and control. Volume 70(2021)
• Publication Date: 2021-09
• Subjects: Brain tissue -- Convolutional neural network -- Deep learning -- Magnetic resonance imaging -- Segmentation; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103063
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A novel 3D DCNN with reduced parameters is proposed for brain tissue segmentation. Dense and residual connections are used for more compact feature representation. Different loss functions are investigated for training the network. The proposed network is shown to provide a superior performance using two datasets. Abstract: Deep convolutional neural networks (DCNN) have proven to be the state-of-the-art methods for brain tissue segmentation; however, their complex architectures, and the large number of parameters make them computationally expensive and difficult to optimize. In this paper, a novel 3D DCNN architecture, which is built upon the U-Net structure, is presented for compact feature representation and efficient parameter reduction in order to segment the brain tissues into white matter, gray matter, and cerebrospinal fluid (Code is available at: https://github.com/basnetr/U-DenseResNet ). The basic idea in the proposed method is to use densely connected convolutional layers and residual skip-connections in order to increase the representation capacity, improve the gradient flow, facilitate easier and better learning, and reduce the number of parameters of the network. The loss functions, cross-entropy, dice similarity, and a combination of the two are used for the training of the proposed network. Experimental results show that the proposed approach provides the best performance on the test dataset of the single-modality IBSR18 dataset containing MR scans of diverse age groups and competitive performance on the multi-modality brain tissue segmentation challenge, iSeg-2017, containing MR scans of infants while reducing, for both the datasets, the parameters ranging from 40% to 98% compared to that of the other deep-learning based architectures. The proposed method significantly reduces the number of parameters of DCNNs while still providing high degree of accuracy. The proposed method can be used for the study of brain structure and development, in detecting a wide range of abnormal tissues, to aid diagnosis, and for guiding surgical procedures.