the benefit of automatic segmentation of

The work of this thesis would not have been possible without the support of many people. Without the coordination and advice of the Chemical and Physical Biology program leaders at Vanderbilt, namely Professors Al Beth, Hassane Mchaourab, and Bruce Damon, my continuation of this work would not have been possible. No one has been more important to my mental health and the completion of this work than my wife, Christina.

This work could never have been completed without her, and for that I will always be grateful.

INTRODUCTION

An even more obvious example is at the border between the brainstem and the spinal cord. We apply the combined transformations to the atlas delineations to create a segmentation of the structure of interest. In radiotherapy, we mostly deal with damage to the retina and the lens of the eye.

Also note that this reviewer appears to have segmented the optic nerve at the level of the pituitary gland (we leave the other expert's chiasm in place for reference).

Figure I.1: Registration of source (atlas) and target (patient) MR images. Each row repre- repre-sents a different inter-subject registration set; source images in column (a) are transformed to patient images in column (e)

CHARACTERIZATION OF SEGMENTATION VARIANCE

COMPARISON OF MANUAL AND AUTOMATIC SEGMENTATION METHODS FOR BRAIN STRUCTURES IN THE PRESENCE OF

SPACE-OCCUPYING LESIONS: A MULTI-EXPERT STUDY

Finally, local non-rigid registration is performed between the local affine registration results and the atlas image. The STAPLE algorithm uses expectation maximization to provide a probabilistic estimate of the ground truth. This means that the distribution of automatic volumes falls within the variation of individual doctors.

Furthermore, the coefficient of variation of the automatic structures, 11–16%, was consistent across all organ structures. Maurer et al.,2003) and then evaluated on the contour points of the automatic segmentation and the doctor segmentation. Figure II.8 shows the proportion of contour points that fall within 2 mm of the simulated ground truth as a function of rater and structure.

This value can be considered the true positive rate, whereby any contour point drawn within a 2 mm shell of the simulated ground truth scores positive. Each of the geometric measures showed that the automatic segmentations fall within the variation of the expert group, which is shown visually in box plots (figures II.4–II.7). Bondiau and colleagues (Bondiau et al., 2005) investigated atlas-based segmentation of the brainstem using MR images from 6 patients and 7 experts.

This is evident from the variance of the distribution of the Dice index (Figure II.6) of the automatic compared to the simulated ground truths, which is smaller than that of the doctors. The automated system was also near the top of the expert group for the 2 mm true positive rate shown in Figure II.8.

Figure II.1: Atlas-based segmentation process for the brainstem and eyes. Panel (a): Orthog- Orthog-onal slices of a patient (top row) with a large right sided lesion and the atlas (bottom row) before registration

IMPACT OF EDITING ON SEGMENTATION VARIANCE AND ACCURACY

SEGMENTATION MODIFICATION IMPROVES EFFICIENCY WHILE REDUCING INTER-EXPERT VARIATION AND

THE PRESENCE OF SPACE-OCCUPYING LESIONS

Much of the methodology in terms of ground truth estimation and metrics was covered in depth in our previous work. A non-rigid local registration is performed between the affine local registration results and the atlas. Each voxel in the distance map contains the Euclidean distance between that voxel and the nearest ground truth edge voxel.

Contour points that were within the boundary of the ground truth were labeled negative, and those outside were labeled positive. Others have achieved normality by transforming data, such as by using the logit function (Zou et al., 2004). Each distribution consists of the pairwise comparisons between the de novo and the edited segmentations for each of the eight raters.

In Figure III.5, it is clear that across all structures and raters, the median DSC increased with editing of the automatic contours. To this end, we compared low-quality regions (slice DSC < 0.5) in the de novo study with the same regions after editing the automatic contours. So far, the results have focused on the performance of the automatic system in the context of editing compared to the experts' de novo segmentations and the unedited automatic segmentations.

The density of outliers within the bands is proportional to the number of outliers outside the plot range. The dotted line is drawn at the median for the raw machine (A1). However, the best results came from editing the automatic segmentations, which were consistent across structures.

This is likely a result of the reduced variance of the A1 edited class compared to the other edited classes.

Figure III.1: Ground truth estimation. The upper left panel displays the area of an optic nerve for patient 12 on an axial CT slice

DOSIMETRIC IMPACT OF AUTOMATIC SEGMENTATION

The sources of the segmentations were 1) the automatic segmentations (A1), 2) their own segmentations (self) and 3) their peers' segmentations (peer) from the first (de novo) study. Then, for each of the remaining 7 judges and A1, the trial was copied and rerun with the only change being that of the normal tissue input. Dose-to-ground truth segmentations were used to evaluate the impact of segmentation differences to our best estimate of the true organs at risk (the ground truth estimates).

Here we evaluate the plans as a function of the difference in dose between the evaluator segmentation (for which the plan was optimized) and the ground-truth segmentations (IV.4). Each black dot represents a difference in maximum dose compared to the average of the assessor's peers. The unit [%] of the y-axis in the Vxx graphs is the absolute difference in percentage volume, not a percentage error.

The minimum and maximum doses for the targets varied slightly more, but their range of variation across all plans, including A1, was less than 2% of the prescription dose. However, there were individual cases that may have been clinically important, with differences in maximum dose as high as 10 Gy more to the brainstem and chiasm than delivered by the rest of the group. The automated system resulted in plans that worked quite well within the variation of the physician-derived plans.

The variation in this analysis was muted compared to that of the dose reporting differences. Maximum dose, although the most commonly used figure of merit for the normal tissues of the brain, is more susceptible to these variations than volume doses.

Table IV.1: Doses to targets from the A 1 -derived plans in comparison to the range of the physician-derived plans.

DISCUSSION AND FUTURE DIRECTIONS OF RESEARCH

Other body sites require much more expert time (more than an hour) for segmentation, especially in the head and neck. In Chapter III, we presented results of a study to test the interaction between the automatic segmenters and the human experts. Even given the results of the de novo study, it is likely that automatic segmentations will be reviewed by the end user.

With this in mind, we tested the interaction of humans and an automated editing system. We were also interested in the effect of editing outside the context of an automatic system. Rather than being limited to traditional anatomical atlases, users can invoke an automated system to incorporate the knowledge base into the atlases of the target patient as a starting point.

Again, we found that the automated system performed well within the context of the experts. This may have important implications for clinical trials and toxicity studies and may explain some of the differences noted between current studies. We propose similar studies to those presented in the present work, but with a different method for data acquisition.

In the previous studies, we collected data through the clinical treatment planning systems in a laborious process that requires a lot of manual intervention to input and output the necessary data structures. This would be an important contribution, especially if an editorial review could be conducted, as previous evaluation work in the head and neck region has been exploratory, using only a few raters and patients (Chao et al.,2007;Stapleford et al .,2010). ).

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