Abstract #1300

Deep Learning Multi-class Segmentation Algorithm is more Resilient to the Variations of MR Image Acquisition Parameters

Yi-Tien Li^1,2, Yi-Wen Chen¹, David Yen-Ting Chen^1,3, and Chi-Jen Chen^1,4

¹Department of Radiology, Taipei Medical University - Shuang Ho Hospital, New Taipei, Taiwan, ²Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan, ³Department of Radiology, Stanford University, Palo Alto, CA, United States, ⁴School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan

A huge amount of T2-FLAIR images with appearance of white matter hyperintencities (WMH) were used. 1368 cases from one hospital were selected as the training set. Another 100 cases from the same hospital and 200 cases from the other 2 different hospitals were treated as the independent test set. Based on multi-class U-SegNet approach, it can achieve the highest F1 score (same hospital: 90.01%; different hospital: 86.52%) in the test set compared with other approaches. The result suggested that the multi-class segmentation approach is more resilient to the variations of MR image parameters than the single label segmentation approach.

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.