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

  • 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/.

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Metadaten
Author:Maximilian Weiherer, Andreas EigenbergerORCiD, Bernhard EggerORCiD, Vanessa BrébantORCiD, Lukas PrantlORCiD, Christoph PalmORCiDGND
URN:urn:nbn:de:bvb:898-opus4-30506
DOI:https://doi.org/10.1007/s00371-022-02431-3
Parent Title (English):The Visual Computer
Publisher:Springer Nature
Document Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2022/03/08
Tag:Breast imaging; Breast reconstruction surgery; Non-rigid surface registration; Statistical shape model; Surgical outcome simulation
Volume:39
Issue:4
First Page:1597
Last Page:1616
Note:
Corresponding author: Christoph Palm
Note:
Zugehörige arXiv-Publikation:
https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/2023
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Biomedical Engineering - RCBE
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
Begutachtungsstatus:peer-reviewed
research focus:Lebenswissenschaften und Ethik
OpenAccess Publikationsweg:Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium
Corresponding author der OTH Regensburg
Funding:DEAL Springer Nature
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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