Abstract #0242

Automatic Rectal Tumor Segmentation and Extramural Venous Invasion Diagnosis based on Deep Learning and Radiomics Model

Jiyao Liu¹, Rencheng Zheng¹, Chengyan Wang², Yigang Pei³, Yinghua Chu⁴, and He Wang¹

¹Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ²Human Phenome Institute, Fudan University, Shanghai, China, ³Xiangya Hospital Central South University, Changsha, Hunan, China, ⁴Siemens Healthineers, Shanghai, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, SegmentationThis study developed an automatic diagnosis model for rectal cancer, which consists following steps: high-precision rectal tumor segmentation by Spatial Hybrid Network (SH-Net) and Adaboost Decision Tree based radiomics model to improve the diagnostic performance of extramural venous invasion (EMVI). The comparable diagnostic performance of the proposed model compared to the visual assessment by radiologists suggests the potential to help doctors with clinical diagnosis of EMVI.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.