Differentiating AD from aging using semiautomated measurement of hippocampal atrophy rates

Neuroimage. 2004 Oct;23(2):574-81. doi: 10.1016/j.neuroimage.2004.06.028.


Manual segmentation of the hippocampus is the gold standard in volumetric hippocampal magnetic resonance imaging (MRI) analysis; however, this is difficult to achieve reproducibly. This study explores whether application of local registration and calculation of the hippocampal boundary shift integral (HBSI) can reduce random variation compared with manual measures. Hippocampi were outlined on the baseline and registered-repeat MRIs of 32 clinically diagnosed Alzheimer's disease (AD) patients and 47 matched controls (37-86 years) with a wide range of scanning intervals (175-1173 days). The scans were globally registered using 9 degrees of freedom and subsequently locally registered using 6 degrees of freedom and HBSI was then calculated automatically. HBSI significantly reduced the mean rate (P < 0.01) and variation in controls (P < 0.001) and increased group separation between AD cases and controls. When comparing HBSI atrophy rates with manually derived atrophy rates at 90% sensitivity, specificities were 98% and 81%, respectively. From logistic regression models, a 1% increase in HBSI atrophy rates was associated with an 11-fold (CI 3, 36) increase in the odds of a diagnosis of AD. For manually derived atrophy rates, the equivalent odds ratio was 3 (CI 2,4). We conclude that HBSI-derived atrophy rates reduce operator time and error, and are at least as effective as the manual equivalent as a diagnostic marker and are a potential marker of progression in longitudinal studies and trials.

Publication types

  • Clinical Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aging / pathology*
  • Algorithms
  • Alzheimer Disease / diagnosis*
  • Alzheimer Disease / pathology*
  • Atrophy
  • Diagnosis, Differential
  • Female
  • Hippocampus / pathology*
  • Humans
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging
  • Male
  • Middle Aged