  We present the Regensburg Breast Shape Model (RBSM) -- a 3D statistical shape
model of the female breast built from 110 breast scans acquired in a standing
position, and the first publicly available. Together with the model, a fully
automated, pairwise surface registration pipeline used to establish dense
correspondence among 3D breast scans is introduced. Our method is
computationally efficient and requires only four landmarks to guide the
registration process. A major challenge when modeling female breasts from
surface-only 3D breast scans is the non-separability of breast and thorax. In
order to weaken the strong coupling between breast and surrounding areas, we
propose to minimize the variance outside the breast region as much as possible.
To achieve this goal, a novel concept called breast probability masks (BPMs) is
introduced. 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 as
accurately as possible inside the breast region and only roughly outside. This
simple yet effective strategy significantly reduces the unwanted variance
outside the breast region, leading to better statistical shape models in which
breast shapes are quite well decoupled from the thorax. The RBSM is thus able
to produce a variety of different breast shapes as independently as possible
from the shape of the thorax. Our systematic experimental evaluation reveals a
generalization ability of 0.17 mm and a specificity of 2.8 mm. To underline the
expressiveness of the proposed model, we finally demonstrate in two showcase
applications how the RBSM can be used for surgical outcome simulation and the
prediction of a missing breast from the remaining one. Our model is available
at https://www.rbsm.re-mic.de/.
