Abstract #1715

Fully Automated Segmentation of Cervical Cancer in Diffusion MR Imaging Using U-Net Convolutional Neural Networks

Yu-Chun Lin^1,2, Chia-Hung Lin¹, Hsin-Ying Lu¹, Ho-Kai Wang¹, Su-Han Ng¹, Jiunjie Wang², and Gigin Lin¹

¹Dept. Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Taiwan, ²Dept. Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan

The aim of the study is to evaluate the performance of U-Net in tumor segmentation on diffusion MR imaging for patients with cervical cancer. Diffusion weighted imaging of b0, b1000 and ADC maps were used for training. The ADC histogram parameters of predicted region of tumor were assessed for accuracy and reproducibility. The results show the triple-channel training algorithm exhibited the best performance in both training and testing datasets. The predicted voxels of tumor can be used to generate the volumetric ADC data for Radiomics study.

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