Impact of Atlas Selection on Clinical Practice

There have been numerous studies investigating atlas-based contouring within the radiotherapy domain. Most of these studies only evaluate quantitative accuracy, yet a number have also considered the clinical impact with respect to contouring time. Table 3.2 shows studies where editing time has been investigated for atlas contouring. Although the atlas selection methods are not described in detail for commercial software, it is known that selection is being performed using mutual information as a similarity criteria by some manufacturers [26, 27].

It can be observed in Table 3.2 that for the prostate there are two studies where larger numbers of atlases have been used together with atlas selection, and two with fewer atlases and no selection. However, the reported percentage time saving is similar regardless of the number of atlases or the use of selection. While timing was not investigated, Lee et al. [28] found no improvement in performance using quantitative measures for a commercial system when increasing the size of an atlas database size from 20 to 100 atlases in steps of 20 atlases. Although that study only investigated two organs in the head and neck, these organs (mandible and thyroid) were chosen as laborious to draw but also represent organs with significant difference in contrast and appearance. Thus, the available evidence suggests that the selection methods implemented by some manufacturers have no impact on the resulting clinical workflow.

Summary and Recommendations for Future Research

In this chapter the background to atlas selection has been reviewed, seeing that since the outset the underlying assumption has been that an atlas with a more similar image to the test case will have better contouring performance than a less similar atlas. It was also seen that while there was some evidence to back up this assumption, it was not fully examined. An experiment was presented to test this assumption whereby a number of image-based atlas selection methods were evaluated and compared to perfect atlas selection. This study reveals that image-based methods fall some way short of perfect selection in terms of rank, and consequently atlas selection is having negligible impact in terms of clinical efficiency.

Notwithstanding this rather negative finding, there is some cause for optimism. Works exploring learned similarity measures have also shown encouraging improvements [15] over the direct image- based measures explored here, and the search for the optimum combination of atlases to generate the best consensus has only recently been considered [11, 24].

Schipaanboord et al., using extreme value theory, suggested that contouring performance equivalent to clinical standards could be possible using a large atlas database in the presence of perfect atlas selection [2]. Thus, there is scope for significant gains to be made with further research into atlas selection. However, as demonstrated in this chapter methods proposed in the future should evaluate their performance not with respect to where they have come from (i.e. older reference implementations) but with respect to where they want to get to (i.e. perfect selection).

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