Abstract #2333

Deep Learning Segmentation of Rectal Cancer on MRI

Endre Grøvik^1,2, Darvin Yi³, Franziska Knuth⁴, Sebastian Meltzer⁵, Anne Negård⁵, and Kathrine Røe Redalen⁴

¹Department for Diagnostic Physics, Oslo University Hospital, Oslo, Norway, ²Faculty of Health Sciences, University of South-Eastern Norway, Drammen, Norway, ³University of Illinois at Chicago, Chicago, IL, United States, ⁴Norwegian University of Science and Technology, Trondheim, Norway, ⁵Akershus University Hospital, Lørenskog, Norway

Treatment of rectal cancer often requires repeated identification of the tumor volume by means of manual delineation by expert radiologists or oncologists. This is a tedious and time-consuming task, particularly with the growing use of multi-sequence 3D imaging. In this work, we have implemented a deep neural network for automatic detection and segmentation of rectal cancer. Our model demonstrates high detection and segmentation performance, equivalent to that of an expert reader, thus illustrating the potential use of deep learning-based segmentation in a clinically relevant setting.

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.