In the NIST lab we develop computer vision image processing algorithms for analysis of medical images that are focused on registration and segmentation. These techniques are applied to different research projects that include:
  • image guided neurosurgery
  • disease diagnosis, and
  • prognosis and quantification for diseases such as multiple sclerosis, epilepsy, schizophrenia and degenerative diseases such as Alzheimer’s dementia.

Great paper from @CassMorrison01 (with @DadarMahsa & @AnitaManera) on WMH load, vascular risk factors and race.
@TheNeuro_MNI @bic_mni @McGillBME @NistLab

For those of you doing the #mastodonmigration having difficulty finding your friends, there are curated lists of academics at
It is easy to grab a list of people to import into the elephant. (I found 500+ neuroscientists!)
See you there!

Great paper from @CassMorrison01 with @DadarMahsa @sylv_villeneuve and @sducharme66 looking at WMH in SCD. We showed that SCD+ and SCD- differences in WMH (wrt total and regional burden) depends on on method to definition of SCD. more at

Cassandra Morrison @CassMorrison01

Excited to announce our newest paper on subjective cognitive decline! This paper shows how different methods used to define SCD may reflect different types of underlying pathologies.
@DadarMahsa @dlouiscollins @sylv_villeneuve @sducharme66

Worried about geometric distortion in MRI?
Hoping for correction methods to improve longitudinal morphological analysis?
@vfonov’s LEGO phanton has been validated in ONDRI

Non-linear registration has many uses, including patient-to-image alignment for image guided surgery. Can such mapping be trusted? My student Josh Bierbrier wrote an extensive literature review on estimating uncertainty in medical image registration.

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