# Evaluating the Effect of Intensity Standardisation
## 4 Discussion  
Recent works have shown that intensity non-standardness contributes to atrophy quantification errors [14]. In this work, we studied whether intensity standardisation could benefit an established and thoroughly validated atrophy quantification tool provided in the FSL package, FSL-SIENA. Given that both registration and segmentation may be affected by intensity variations, we tested the effect of incorporating intensity standardisation before registration and segmentation and before segmentation only. We considered six intensity standardisation techniques comprising z-score, fuzzy c-means, Gaussian mixture model, kernel density estimation, WhiteStripe and histogram matching. To our knowledge, this is the first time that the effect and suitability of multiple intensity standardisation has been quantitatively investigated for cerebral atrophy quantification. We examined the degree of similarity between histograms of intensity of baseline and follow-up scans before and after standardisation as a measure of the quality of the standardisation process per se. Given that an intensity standardisation method maps histograms to a normalised space, we expected its use to lead to lower KL divergence  
values. Indeed, we observed that inter-subject inter-scan variability was reduced with the use of most intensity standardisation methods, except for WhiteStripe for which the KL divergence was comparable to that measure without standardisation. A closer examination of the histograms of intensity in Figures A1 and A2 revealed that standardising intensities with WhiteStripe led to high coincidence in the white matter mode, but less coincidence in grey matter and cerebrospinal fluid modes. A similar observation was highlighted by [PERSON] et al. [28]. We assessed the effect of standardisation on the scan-rescan repeatability of FSL-SIENA by examining atrophy detected in scans of the same subject taken during the same imaging session. Given that the level of atrophy is expected to be minimal during short periods of time, a large deviation from zero would suggest the quantification method--not the standardisation method--is sensible to subtle imaging variations. Thus, we tested whether scan-rescan errors were similar before and after standardising intensities. Most intensity standardisation methods lead to median scan-rescan errors similar to those measured on raw scans, except for WhiteStripe (\(p<0.001\)). Furthermore, standardising before registration and segmentation resulted in increased variance in scan-rescan errors, especially for z-score and WhiteStripe. Even though we did not detect a clear misregistration problem, we noticed subtle misalignment in a few cases that may have resulted in the measured errors. We evaluated the effect of intensity standardisation on atrophy differences--not on histograms of intensity--gauged by FSL-SIENA between Alzheimer's disease and normal control subjects from ADNI and OASIS. A larger effect size implies that smaller sample sizes are required to show statistical significance between groups. We opted for this approximation as assessing accuracy in longitudinal atrophy quantification is difficult [27; 29] due to the lack of manual segmentations and histological confirmation. According to our experimental results, intensity standardisation affected the estimation of brain atrophy. The atrophy gauged on standardised scans was larger than that measured on raw scans. Whether one or the other is more reflective of the disease cannot be clarified in this study. However, it is important to remark that mean annual brain volume changes detected on images standardised with multiple methods were similar. Furthermore, the use of z-score and WhiteStripe led to consistently higher effect sizes compared to those obtained without standardisation if applied after registration and before segmentation. We noticed that their use prior to registration led to a clear misregistration in numerous cases.