.png){kind=icon}
返回
RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compbiomed.2022.106426.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Brain tumors are one of the most fatal cancers. Magnetic Resonance Imaging (MRI) is a non-invasive method that provides multi-modal images containing important information regarding the tumor. Many contemporary techniques employ four modalities: T1-weighted (T1), T1-weighted with contrast (T1c), T2-weighted (T2), and fluid-attenuation-inversion-recovery (FLAIR), each of which provides unique and important characteristics for the location of each tumor. Although several modern procedures provide decent segmentation results on the multimodal brain tumor image segmentation benchmark (BraTS) dataset, they lack performance when evaluated simultaneously on all the regions of MRI images. Furthermore, there is still room for improvement due to parameter limitations and computational complexity. Therefore, in this work, a novel encoder-decoder-based architecture is proposed for the effective segmentation of brain tumor regions. Data pre-processing is performed by applying N4 bias field correction, z-score, and 0 to 1 resampling to facilitate model training. To minimize the loss of location information in different modules, a residual spatial pyramid pooling (RASPP) module is proposed. RASPP is a set of parallel layers using dilated convolution. In addition, an attention gate (AG) module is used to efficiently emphasize and restore the segmented output from extracted feature maps. The proposed modules attempt to acquire rich feature representations by combining knowledge from diverse feature maps and retaining their local information. The performance of the proposed deep network based on RASPP, AG, and recursive residual (R2) block termed RAAGR2-Net is evaluated on the BraTS benchmarks. The experimental results show that the suggested network outperforms existing networks that exhibit the usefulness of the proposed modules for finesegmentation. The code for this work is made available online at: https://github.com/Rehman1995/RAAGR2-Net.
Keyword:
Magnetic Resonance Imaging (MRI)
Multimodal brain tumor image segmentation
benchmark (braTS)
Residual Atrous Spatial Pyramid Pooling
(RASPP)
Attention gate (AG)
Recursive residual (R2) block
期刊
IF:
6.3
论文数:
8.3K
被引数:
3.3W
机构
引用论文
分享收藏KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
OAAI
OAAI分享收藏分享收藏BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder-Decoder NetworkBrainseg-net: 通过增强的编码器-解码器网络进行脑肿瘤MR图像分割.png)
DIAGNOSTICS
OAAI
DIAGNOSTICSIF3.3
OAAI分享收藏
查看更多内容