A Deep Fully Convolutional Network for Distal Radius and Ulna Semantic Segmentation | Zendy

Shuqiang Wang | Zendy; Wei Liang | Zendy; Hongfei Wang | Zendy; Zhuo Chen | Zendy; Yiqian Lu | Zendy

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A Deep Fully Convolutional Network for Distal Radius and Ulna Semantic Segmentation

Author(s) -

Shuqiang Wang,

Wei Liang,

Hongfei Wang,

Zhuo Chen,

Yiqian Lu

Publication year - 2019

Publication title -

iop conference series materials science and engineering

Language(s) - English

Resource type - Journals

eISSN - 1757-899X

pISSN - 1757-8981

DOI - 10.1088/1757-899x/646/1/012025

Subject(s) - segmentation , computer science , intersection (aeronautics) , ulna , radius , artificial intelligence , image segmentation , pattern recognition (psychology) , cartography , anatomy , geography , medicine , computer network

Semantic segmentation is an essential step to do further image analysis and scene understanding tasks. In medical imaging analysis applications, it is even more challenging to do automatic segmentation due to tissues’ complicated boundaries. In this paper, a fully convolutional network (FCN) based model is constructed to segment distal radius and ulna (DRU) areas from hand X-ray images. We evaluated the proposed network on a clinical DRU dataset with different network configurations. The proposed network can achieve 98% accuracy and 96% mean Intersection over Union (IoU).

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