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Abstract #0305

Automatic Liver Tumor Detection using Deep learning based segmentation and Radiomics guided Candidate Filtering

Rencheng Zheng1, Qidong Wang2, Ziying Feng3, Chengyan Wang3, and He Wang1,3
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Radiology department, The first affiliated hospital, College of medicine, Zhejiang University, Hangzhou, China, 3Human Phenome Institute, Fudan University, Shanghai, China

The objective of this study is to perform automatic multi DCE phases liver tumor detection using deep learning based segmentation and radiomics guided candidate filtering. The proposed model consists mainly of two stages, primary segmentation based on a U-net architecture neural network in stage1, and suspected tumor discrimination mechanism using multi DCE phases radiomics features including shape features, texture features, time dimension features and location information in stage 2. The proposed two-stage model exhibits superior performance in HCC tumor segmentation with a mean Dice score of 0.7928 in test set.

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