Efficient symmetry-driven fully convolutional network for multimodal brain tumor segmentation
Haocheng Shen,Jianguo Zhang,Wei-Shi Zheng +2 more
- pp 3864-3868
TLDR
A novel and efficient method for brain tumor (and sub regions) segmentation in multimodal MR images based on a fully convolutional network (FCN) that enables end-to-end training and fast inference.Abstract:
In this paper, we present a novel and efficient method for brain tumor (and sub regions) segmentation in multimodal MR images based on a fully convolutional network (FCN) that enables end-to-end training and fast inference. Our structure consists of a downsampling path and three upsampling paths, which extract multi-level contextual information by concatenating hierarchical feature representation from each upsam-pling path. Meanwhile, we introduce a symmetry-driven FCN by the proposal of using symmetry difference images. The model was evaluated on Brain Tumor Image Segmentation Benchmark (BRATS) 2013 challenge dataset and achieved the state-of-the-art results while the computational cost is less than competitors.read more
Citations
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Book ChapterDOI
Boundary-aware fully convolutional network for brain tumor segmentation
TL;DR: A novel, multi-task, fully convolutional network (FCN) architecture for automatic segmentation of brain tumor achieves improved segmentation performance by incorporating boundary information directly into the loss function.
Journal ArticleDOI
Brain tumor segmentation with deep convolutional symmetric neural network
TL;DR: A novel deep convolutional neural network which combines symmetry have been proposed to automatically segment brain tumors, called Deep Convolutional Symmetric Neural Network (DCSNN), extends DCNN based segmentation networks by adding symmetric masks in several layers.
Journal ArticleDOI
Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation
Sajid Iqbal,Sajid Iqbal,Muhammad Usman Ghani Khan,Tanzila Saba,Zahid Mehmood,Nadeem Javaid,Amjad Rehman,Rashid Abbasi +7 more
TL;DR: This research presents deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images and uses class weighting to cope with the class imbalance problem.
Journal ArticleDOI
Deep learning-based detection and segmentation-assisted management of brain metastases.
Jie Xue,Bao Wang,Yang Ming,Xuejun Liu,Zekun Jiang,Chengwei Wang,Xiyu Liu,Ligang Chen,Jianhua Qu,Shangchen Xu,Xuqun Tang,Ying Mao,Yingchao Liu,Dengwang Li +13 more
TL;DR: The BMDS net yields the accurate detection and segmentation of BMs automatically and could assist stereotactic radiotherapy management for the diagnosis, therapy planning and follow up.
Journal ArticleDOI
Deep Learning for Hemorrhagic Lesion Detection and Segmentation on Brain CT Images
Lu Li,Meng Wei,Bo Liu,Kunakorn Atchaneeyasakul,Fugen Zhou,Zehao Pan,Shimran A. Kumar,Jason Y. Zhang,Yuehua Pu,David S Liebeskind,Fabien Scalzo +10 more
TL;DR: The results demonstrate the effectiveness, robustness, and advantages of the proposed deep learning model in automatically hemorrhage lesion diagnosis, which make it possible to be a clinical decision support tool in stroke diagnosis.
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