Clinical evaluation of atlas based segmentation for radiotherapy of prostate tumours

Sammanfattning: Abstract   Background Semi-automated segmentation using deformable registration of atlases consisting of pre-segmented patient images can facilitate the tedious task of delineating structures and organs in patients subjected to radiotherapy planning. However, a generic atlas based on a single patient may not function well enough due to the anatomical variation between patients. Fusion of segmentation proposals from multiple atlases has the potential to provide a better segmentation due to a more complete representation of the anatomical variation. Purpose The main goal of the present study was to investigate potential operator timesavings from use of atlas-based segmentation compared to manual segmentation of patients with prostate cancer. It was also anticipated that, and evaluated if, the use of semi-automated segmentation workflows would reduce the operator dependent variations in delineation. Materials and Methods A commercial atlas-based segmentation software (VelocityAI from Nucletron AB) was used with several atlases of consistently, protocol based, delineated CT images to create multiple-atlas segmentation proposals through deformable registration. The atlas that was considered most representative was selected to construct single generic atlas segmentation proposals. For fusion of the multiple-atlas segmentations an in-house developed algorithm, which includes information of local registration success was used in a MATLAB-environment[1]. The algorithm used weighted distance map calculations where weights represent probabilities of improving the segmentation results. Based on results from Sjöberg and Ahnesjö the probabilities were estimated using the cross correlation image similarity measure evaluated over a region within a certain distance from the segmentation. 10 patients were included in the study. Each patient was delineated three times, (a) manually by the radiation oncologist, (b) with a generic single-atlas segmentation and (c) with a fusion of multiple-atlas segmentations. For the methods (b) and (c) the radiation oncologist corrected the proposed segmentations blindly without using the result from method (a) as reference. The total number of atlases used for case (c) was 15. The operator time spent by the radiation oncologist was recorded separately for each method. In addition a grading was used to score how helpful the segmentation proposals were for the delineations. The Dice Similarity Coefficient, the Hausdorff distance and the segmented volumes were used to evaluate the similarity between the delineated structures and organs. Results An average time reduction of 26% was found when the radiation oncologist corrected the multiple atlas-based segmentation proposals as compared to manual segmentations. Due to more accurate segmentations and more time saved, segmentation with fused multiple-atlases (c) was superior to the generic single-atlas (b) method, which showed a time reduction of 17%. Hints of an affected intra- and inter-operator variability were seen. Conclusions Atlas-based segmentation saves time for the radiation oncologist but the segmentation proposals always need editing to be approved for dose planning. The atlases, the fusion of these and the software implementation needs to be improved for optimal results and to extend the clinically usefulness.