A rigged model of the breast for preoperative surgical planning
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Abstract (arXiv)
In breast surgical practice, drawing is part of the preoperative planning procedure and is essential for a successful operation. In this study, we design a pipeline to assist surgeons with patient-specific breast surgical drawings. We use a deformable torso model containing the surgical patterns to match any breast surface scan. To be compatible with surgical timing, we build an articulated model through a skinning process coupled with shape deformers to enhance a fast registration process. On one hand, the scalable bones of the skinning account for pose and morphological variations of the patients. On the other hand, pre-designed artistic blendshapes create a linear space for guaranteeing anatomical variations. Then, we apply meaningful constraints to the model to find a trade-off between precision and speed. The experiments were conducted on 7 patients, in 2 different poses (prone and supine) with a breast size ranging from 36A and 42C (US/UK bra sizing). The acquisitions were obtained using the depth camera Structure Sensor, and the breast scans were acquired in less than 1 minute. The result is a registration method converging within a few seconds (3 maximum), reaching a Mean Absolute Error of 2.3 mm for mesh registration and 8.0 mm for breast anatomical landmarks. Compared to the existing literature, our model can be personalized and does not require any database. Finally, our registered model can be used to transfer surgical reference patterns onto any patient in any position.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 May 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
3 papers.
- 2022Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanicscited by 5
- 2021Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scanscited by 3
- 2025Learning Neural Parametric 3D Breast Shape Models for Metrical Surface Reconstruction From Monocular RGB Videoscited by 0
- 2025Learning Neural Parametric 3D Breast Shape Models for Metrical Surface Reconstruction From Monocular RGB Videos15 Oct 2025
- 2022Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics1 Dec 2022
- 2021Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans28 Jul 2021