Abstract #0327

Anatomical-aware Siamese Deep Network for Prostate Cancer Detection on Multi-parametric MRI

Haoxin Zheng^1,2, Miao Qi^2,3, Alex Ling Yu Hung^1,2, Kai Zhao², Steven Raman², and Kyunghyun Sung²

¹Computer Science, University of California, Los Angeles, Los Angeles, CA, United States, ²Radiological Science, University of California, Los Angeles, Los Angeles, CA, United States, ³Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China

Synopsis

Keywords: Prostate, Cancer, Machine Learning

The study aimed to build a deep-learning-based prostate cancer (PCa) detection model integrating the anatomical priors related to PCa’s zonal appearance difference and asymmetric patterns of PCa. A total of 220 patients with 246 whole-mount histopathology (WMHP) confirmed clinically significant prostate cancer (csPCa), and 432 patients with no indication of lesions on multi-parametric MRI (mpMRI) were included in the study. A proposed 3D Siamese nnUNet with self-designed Zonal Loss was implemented, and results were evaluated using 5-fold cross-validation. The proposed model that is aware of PCa-related anatomical information performed the best on both lesion-level detection and patient-level classification experiments.

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.