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 Wang^1,3

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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