Abstract #0948

Automatic segmentation of glioma based on MRI K-space data

Yikang Li^1,2,3, Zhan li Hu^1,2,3, Sen Jia^1,2,3, Wenjing Xu⁴, Zongyang Li^1,2,3, Hairong Zheng^1,2,3, Xin Liu^1,2,3, and Na Zhang^1,2,3

¹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ³CAS key laboratory of health informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ⁴Faculty of Information Technology, Beijing University of Technology, Beijng, China

Nowadays, Magnetic Resonance Imaging (MRI) plays a pivotal role in gliomas diagnosis, analysis, and surgery planning. Nevertheless, the accuracy of MRI segmentation is enormously restricted by the quality of images. Therefore, we demonstrate a new method that can directly make segmentations from K space data. And the results show that our method achieves the state of the art.

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