Atlas-based segmentation in breast cancer radiotherapy: Evaluation of specific and generic-purpose atlases. (April 2017) - Ciardo, Delia; Gerardi, Marianna Alessandra; Vigorito, Sabrina; Morra, Anna; Dell'acqua, Veronica; Diaz, Federico Javier; Cattani, Federica; Zaffino, Paolo; Ricotti, Rosalinda; Spadea, Maria Francesca; Riboldi, Marco; Orecchia, Roberto; et al.
Abstract: Objectives: Atlas-based automatic segmentation (ABAS) addresses the challenges of accuracy and reliability in manual segmentation. We aim to evaluate the contribution of specific-purpose in ABAS of breast cancer (BC) patients with respect to generic-purpose libraries. Materials and methods: One generic-purpose and 9 specific-purpose libraries, stratified according to type of surgery and size of thorax circumference, were obtained from the computed tomography of 200 BC patients. Keywords about contralateral breast volume and presence of breast expander/prostheses were recorded. ABAS was validated on 47 independent patients, considering manual segmentation from scratch as reference. Five ABAS datasets were obtained, testing single-ABAS and multi-ABAS with simultaneous truth and performance level estimation (STAPLE). Center of mass distance (CMD), average Hausdorff distance (AHD) and Dice similarity coefficient (DSC) between corresponding ABAS and manual structures were evaluated and statistically significant differences between different surgeries, structures and ABAS strategies were investigated. Results: Statistically significant differences between patients who underwent different surgery were found, with superior results for conservative-surgery group, and between different structures were observed: ABAS of heart, lungs, kidneys and liver was satisfactory (median values: CMD<2 mm, DSC≥0.80, AHD<1.5 mm), whereas chest wall, breast and spinal cord obtained moderate performance (median values: 2 mm ≤ CMD<5 mm, 0.60 ≤ DSC<0.80, 1.5 mm ≤ AHD<4 mm) and esophagus, stomach, brachial plexus and supraclavicular nodes obtained poor performance (median CMD≥5 mm, DSC<0.60, AHD≥4 mm). The application of STAPLE algorithm generally yields higher performance and the use of keywords improves results for breast ABAS. Conclusion: The homogeneity in the selection of atlases based on multiple anatomical and clinical features and the use of specific-purpose libraries can improve ABAS performance with respect to generic-purpose libraries. Highlights: Evaluation of a commercial multi-atlas based auto-segmentation of breast cancer. Use of generic/specific-purpose libraries for target and organs at risk segmentation. Preliminary time analysis of ABAS editing.