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Abstract:
Brain tumor segmentation is one of the main challenging problems in computer vision and its early diagnosis is critical to clinics. Segmentation needs to be accurate, efficient and robust to avoid influences caused by various large and complex biases added to images. This paper proposes a multiple convolutional neural network (CNNs) framework with discrimination mechanism which is effective to achieve these goals. First of all, this paper proposes to construct different triplanar 2D CNNs architecture for 3D voxel classification, greatly reducing segmentation time. Experiment is conducted on images provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 for both training and testing. As T1, T1-enhanced, T2 and FLAIR MRI images are utilized, multimodal features are combined. As a result, accuracy, sensitivity and specificity are comparable in comparison with manual gold standard images and better than state-of-the-art segmentation methods.
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Source :
2015 INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING (IIH-MSP)
Year: 2015
Page: 306-309
Language: English
Cited Count:
WoS CC Cited Count: 41
SCOPUS Cited Count: 61
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 0
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