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We present the Regensburg Breast Shape Model (RBSM) -- a 3D statistical shapemodel of the female breast built from 110 breast scans acquired in a standingposition, and the first publicly available. Together with the model, a fullyautomated, pairwise surface registration pipeline used to establish densecorrespondence among 3D breast scans is introduced. Our method iscomputationally efficient and requires only four landmarks to guide theregistration process. A major challenge when modeling female breasts fromsurface-only 3D breast scans is the non-separability of breast and thorax. Inorder to weaken the strong coupling between breast and surrounding areas, wepropose to minimize the variance outside the breast region as much as possible.To achieve this goal, a novel concept called breast probability masks (BPMs) isintroduced. A BPM assigns probabilities to each point of a 3D breast scan,telling how likely it is that a particular point belongs to the breast area.During registration, we use BPMs to align the template to the target asaccurately as possible inside the breast region and only roughly outside. Thissimple yet effective strategy significantly reduces the unwanted varianceoutside the breast region, leading to better statistical shape models in whichbreast shapes are quite well decoupled from the thorax. The RBSM is thus ableto produce a variety of different breast shapes as independently as possiblefrom the shape of the thorax. Our systematic experimental evaluation reveals ageneralization ability of 0.17 mm and a specificity of 2.8 mm. To underline theexpressiveness of the proposed model, we finally demonstrate in two showcaseapplications how the RBSM can be used for surgical outcome simulation and theprediction of a missing breast from the remaining one. Our model is availableat https://www.rbsm.re-mic.de/.

Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans