A rigged model of the breast for preoperative surgical planning

Mazier, Arnaud; Ribes, Sophie; Gilles, Benjamin; Bordas, Stéphane P.A.

doi:10.1016/j.jbiomech.2021.110645

Cited by 11 publications

(4 citation statements)

References 36 publications

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“…1. Note that the problem of only quasi-similar postures was also recently observed by Mazier et al [38]. All in all, the aforementioned factors definitely hinder the implementation of large-scale, high-quality data surveys and might also explain why no publicly available data set of female 3D breast scans exits.…”

Section: Challengesmentioning

confidence: 72%

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

et al. 2022

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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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Section: Challengesmentioning

confidence: 72%

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

et al. 2022

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“…The ability to make fast or real-time predictions of the response of physical systems is essential for a variety of engineering applications. Notable examples of this can be found in the field of robotics (Rus and Tolley, 2015;Choi et al, 2021) and medical simulations (Cotin et al, 1999;Courtecuisse et al, 2014;Bui et al, 2018;Mazier et al, 2021), which have the potential to advance personalized medicine and improve computer-assisted and robotic surgery (Chen et al, 2020;Dennler et al, 2021). In computational physics and chemistry, fast and accurate predictions are fundamental for studying complex systems, such as those arising in biology and materials science (Friesner, 2005), or in drug discovery (De Vivo et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics

et al. 2023

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Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains a significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures for efficient learning of highly non-linear deformations of solid bodies. The first two architectures are based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown promising performance for learning on mesh-based data. The third architecture is Perceiver IO, a very recent architecture that belongs to the family of attention-based neural networks–a class that has revolutionised diverse engineering fields and is still unexplored in computational mechanics. We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies.

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“…Furthermore, its compatibility to open-source meshers like GMSH make its use appealing. The project has already shown is capacity solving large deformation problems (Mazier et al [24]) and mixed formulations (Urcun et al [18,4], Bulle [25]). Previous work provided the implementation of poro-mechanics within the FEniCS project (Haagenson et al [26], Joodat et al [27]).…”

Section: Introductionmentioning

confidence: 99%

Multi-compartment poroelastic models of perfused biological soft tissues: implementation in FEniCSx

Lavigne¹,

Urcun²,

Rohan³

et al. 2023

Preprint

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A rigged model of the breast for preoperative surgical planning

Cited by 11 publications

References 36 publications

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

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

Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics

Multi-compartment poroelastic models of perfused biological soft tissues: implementation in FEniCSx

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