Keywords: Diagnosis/Prediction, Segmentation
Motivation: Public challenge datasets are beneficial to improve classification and segmentation by computer aided diagnosis (CAD); transfer learning may further improve CAD clinical performance.
Goal(s): To investigate if transfer learning can improve the accuracy of whole-prostate and lesion segmentation in multi-parametric MRI.
Approach: Two nnU-Net networks for whole-prostate and lesion segmentation were initially trained on the PROSTATEx public dataset, then fine-tuned with an in-house clinical dataset.
Results: Fine-tuning on clinical data improved the mean Dice score for whole-prostate segmentation from 0.783 to 0.898. However, lesion segmentation networks underperformed due to dataset variability, indicating that while transfer learning is promising, lesion segmentation needs further refinement.
Impact: A nnU-Net network trained on large public datasets, then fine-tuned with a small clinical dataset improved whole-prostate segmentation. This network will facilitate processing requiring whole-prostate masks, such as Quantitative Susceptibility Mapping, and could potentially reduce radiological workload or automate quantification.
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.
Keywords