Improving the BCCT.core Model With Lateral Information

is considered the gold standard of breast cancer treatment. However, the aesthetic outcome is diverse and very difficult to evaluate in a consistent way partly due to the weak reproducibility of the subjective methods in use. This motivated the research on the objective methods. BCCT.core is a very recent software that objectively and automatically evaluates the aesthetic outcome of BCCT. However, as in other approaches, the system only uses frontal patient information, disregarding volumetric perception on lateral measurements. In the current work we investigate the improvement of the BCCT.core model by introducing lateral information extracted from patients images. We compare the performance of the model currently used on BCCT.core with the model developed in this study. Experimental results suggest that with lateral measurements the model presents better performance, however improvements are not significant. We can conclude that is essential to use robust models on the BCCT, and the input of 3D models will probably help to obtain better results. I