Reliability of automated segmentations

A summary of the results of the leave-one-out cross validation are shown in table 8.3. The use of both T1-weighted scans along with FA maps resulted in higher mean dice scores on both the right and left sides. The differences were subtle, but reached statistical significance.

8.3. Results 130 Table 8.3: Similarity metrics for automated segmentations using T1-weighted scans only vs manual segmentations and automated segmentations using FA maps in addition to T1-weighted scans vs manual segmentations. Mean [95% confidence interval] shown

T1 only T1 & FA map p-value

Left Dice 0.940 [0.937, 0.944] 0.942 [0.939, 0.945] 0.02 Mean surface distance 0.334 [0.32, 0.348] 0.329 [0.316, 0.342] 0.02 Hausdorff distance 2.295 [2.153, 2.437] 2.302 [2.141, 2.463] 0.87 Right Dice 0.941 [0.938, 0.944] 0.949 [0.947, 0.951] <0.001 Mean surface distance 0.329 [0.317, 0.341] 0.299 [0.289, 0.308] <0.001 Hausdorff distance 2.220 [2.105, 2.335] 2.081 [1.944, 2.218] 0.01

Examples of the best and worst automatic segmentation (according to the dice overlap score) when using T1-weighted scans only is shown in figure 8.3.2 and when using FA maps in addition to T1-weighted scans is shown in figure 8.12. The scan with the biggest difference in dice overlap scores between the region automatically segmented using T1-weighted scans only and the region automatically segmented using both FA-maps and T1-weighted scans is shown in figure 8.13.

Figure 8.11: Best (top row) and worst (bottom row) automated segmentation (ac- cording to dice score) when using T1-weighted scans only. Blue: in- cluded in manual segmentation only, green: included in both manual and automated segmentations, yellow: included in automated segmen- tation only. The best segmentation had a dice score of 0.96 and the worst had a dice score of 0.89.

8.3. Results 132

Figure 8.12: Best (top row) and worst (bottom row) automated segmentation (ac- cording to dice score) when using FA maps and T1-weighted scans. Blue: included in manual segmentation only, green: included in both manual and automated segmentations, yellow: included in automated segmentation only. The best segmentation had a dice score of 0.96 and the worst had a dice score of 0.90.

Figure 8.13: Scan with the biggest difference in dice scores between the automat- ically generated thalamic region using T1-weighted scans (shown in blue) and the automatically generated thalamic region using both FA maps and T1-weighted scans (shown in yellow). The manually seg- mented region is shown in green. The dice overlap between the man- ually segmented region and the region automatically segmented using T1-weighted scans only was 0.91 whilst for the manually segmented region and the automatically segmented region using both FA and T1- weighted scans it was 0.95.

8.4. Discussion 134

8.4

Discussion

Good levels of reproducibility were achieved when segmenting thalami us- ing T1-weighted scans only and when using coloured FA maps in addition to T1-weighted scans. Modest, but non-significant improvements in dice scores and mean surface distance were seen when comparing the manual segmenta- tions based on T1-weighted scans alone with those based on T1-weighted and coloured FA maps.

The dice scores achieved from the automated segmentations in this study (0.94 when using T1-weighted scans only and 0.95 when using FA maps in addition to T1-weighed scans) were higher than dice scores reported in the literature for the thalamus. The most widely used thalamic segmentation method in AD studies, FIRST, reported a mean dice score of approximately 0.88 (number taken from graphical representation) [Patenaude et al., 2011]. The template library used by the FIRST algorithm includes scans from 336 subjects with range of pathologies, including Alzheimer’s diease, as well as healthy controls. No information is given about the manual thalamic segmen- tation protocol that was followed nor the field strength of the MRI scanners used making comparisons difficult. The dice scores presented in this chap- ter were also higher than those achieved when using the STEPS algorithm in combination with a different template library 0.89 [Cardoso et al., 2015]. The template library used in [Cardoso et al., 2015] was a set of 30 manually-labelled brain scans from healthy volunteers aged between 20 and 54 years of age, de- scribed in detail in [Hammers et al., 2003,Hammers et al., 2007]. A description of the thalamic region definition used is given in [Hammers et al., 2003] and is accordance with what was included in the protocol developed in this chapter. Possible reasons for the superior dice score achieved in the study in this chap- ter could be the larger size of the template library used, better scan quality and tissue contrast (scans were acquired on 3T scanners in this study whilst those used in [Cardoso et al., 2015] were acquired on a 1.5T scanner [Hammers et al., 2003]) or more reliable manual segmentations (an example of one of the

manually labelled brains is given in [Cardoso et al., 2015] and although the slice shown does not include the thalamus, the regions shown appear to have somewhat irregular and biologically implausible boundaries). In addition, a modest, but statistically significant improvement in the mean dice score was observed when using both FA maps and T1-weighted maps in the automated pipeline as opposed to T1-weighted maps alone.

One recent study also utilized data from diffusion scans, in addition to T1-weighted and T2-weighted scans, to automatically segment the tha- lamus [Glaister et al., 2017]. In the method described by Glaister et al., a thalamic region of interest is first obtained, by using a multi-atlas segmenta- tion approach. A set of features, incorporating information from diffusion, T1 and T2-weighted scans is then computed at each voxel within the ROI. This is then used to classify voxels as belonging to the thalamus, or not. Using this technique, the authors reported mean dice scores of 0.88 and 0.89 for the left and right thalami respectively, using leave-one-out cross validation. The dice scores achieved in leave-one-out cross-validation in this chapter were higher (0.94 using T1-weighted imaging only and 0.95 using FA-maps in addition to T1-weighted imaging), although a head-to-head comparison of the two tech- niques has not been performed.

In this study, a manual segmentation protocol with good reproducibil- ity was developed. A template library of thalamic segmentations in subjects with a range of pathologies was generated based on this protocol. The use of this template library in combination with the STEPS algorithm was shown to produce good quality automatic thalamic segmentations.

Chapter 9

Investigation into thalamic volume and

diffusion metrics in mild cognitive

impairment and Alzheimer’s disease

9.1

Introduction

As discussed in chapter 8, there is evidence that the thalamus may be affected by Alzheimer’s disease pathology. Braak and Braak found that the antero- dorsal nucleus was severely affected by Alzheimer’s disease pathology post- mortem [Braak and Braak, 1991]. Imaging studies have shown reduced tha- lamic volumes in subjects with Alzheimer’s disease [De Jong et al., 2008] and studies in pre-symptomatic mutation carriers for familial Alzheimer’s disease have shown diffusion changes in the thalamus prior to symptom onset [Ryan et al., 2013]..

What is not known is at what stage in the disease process the thalami are affected in sporadic Alzheimer’s disease, if there are thalamic diffusion dif- ferences in subjects with sporadic Alzheimer’s disease (AD) and if diffusion metrics are predictive of subsequent atrophy. This may aid early diagnosis of disease and have implications for outcome measures of trials in early (preclin- ical) AD.

1. Investigate thalamic and hippocampal volumes in subjects with subjec- tive memory complaints, early mild cognitive impairment, late mild cog- nitive impairment and established Alzheimer’s disease. Hippocampal volumes were used as a comparator to assess whether the thalamus is affected at a similar stage to the hippocampus.

2. Investigate whether thalamic and hippocampal diffusion metrics can aid in group differentiation above volumetric measures.

3. Investigate whether diffusion metrics are predictive of subsequent whole- brain and hippocampal atrophy rates.

9.2

Methods