# Evaluating the Effect of Intensity Standardisation
## 1 Introduction  
Longitudinal brain atrophy quantification is an active research area in medical image analysis as these measurements permit studying brain development, diagnosing brain diseases and evaluating disease progression over time, and assessing treatment effectiveness [1; 2; 3; 4; 5; 6]. Although slow shrinkage of the brain comes with ageing, it may also be a neuroimaging feature of pathologies, such as schizophrenia, cerebral small vessel disease, Alzheimer's disease and multiple sclerosis [7; 8; 9; 10; 11; 12]. Therefore, accurate and reliable brainvolume measurements are essential for characterising normal and abnormal brain tissue changes and understanding the nature of brain problems.  
Longitudinal brain volumetry assessments consist of finding and quantifying brain tissue variations between two scans of the same patient taken at different time-points: a _baseline_ and a _follow-up_ scan, e.g., scans acquired at inclusion and a year later. Numerous algorithms have been proposed for carrying out such evaluations [8]. However, FSL-SIENA (Structural Image Evaluation, using Normalisation, of Atrophy) [13] continues to be a widespread tool in the medical community, given the fact that it is fully automated and available under open source license [8].  
Like many other methods, FSL-SIENA computes brain edge displacement as a surrogate measure of atrophy [8]. The processing pipeline comprises skull stripping, registration, tissue segmentation and brain edge displacement estimation. Naturally, the accuracy and precision of each step determines the overall performance of FSL-SIENA. For example, intensity variations between baseline and follow-up scans--e.g., caused by imaging protocol--may have serious consequences on both the registration and segmentation steps, affecting subsequent stages of the analysis [14; 15].  
Previous works have explored ways to reduce intensity variations within FSL-SIENA [14; 16]. [PERSON] et al. [16] demonstrated the effectiveness of histogram matching for improving multiple sclerosis lesion segmentation in a multi-site multi-scanner setup. [PERSON] et al. [14] showed the relevance of improved brain extraction and intensity correction modules. In both aforementioned works, the intensity standardisation step consisted of a piece-wise linear histogram matching, which assumes that the balance of tissue classes is consistent between subjects being matched. However, this assumption does not necessarily hold in longitudinal studies [17].  
In this work, we evaluate the effect of intensity standardisation strategies on longitudinal atrophy quantification. In particular, we consider six strategies that are used in medical image analysis; all of them available to the public [17; 18]. We hypothesise that incorporating intensity standardisation in atrophy quantification assessments leads to significantly better estimations compared to when omitted. To our best knowledge, this is the first time such an analysis has been carried out. The contributions of this work are threefold: (i) we showcase and make publicly available a ready-to-use tool for assessing the effect of intensity standardisation on scan-rescan and longitudinal atrophy quantification, (ii) we benchmark six intensity standardisation techniques for harmonising intensities between baseline and follow-up scans within a standard whole brain atrophy quantification pipeline and (iii) we show quantitatively that intensity standardisation may lead to improved longitudinal atrophy quantification.