Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation

Lacher, Rene; Vasconcelos, Francisco; Williams, Norman; Rindermann, Gerrit; Hipwell, John H.; Hawkes, David J.; Stoyanov, Danail

doi:10.1016/j.media.2019.01.003

Cited by 14 publications

(5 citation statements)

References 60 publications

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“…One of the most widely used methods is the Optimal Step Non-rigid Iterative Closest Point (NICP) framework proposed by Amberg et al [4] and based on the work of Allen et al [2]. NICP is the only method already used in the female breast shape domain to reconstruct a 3D breast model from a sequence of depth images [33]. Moreover, NICP was employed to construct the BFM and LSFM.…”

Section: Statistical Shape Models Of the Female Breastmentioning

confidence: 99%

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: Statistical Shape Models Of the Female Breastmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…One of the most widely used methods is the Optimal Step Non-rigid Iterative Closest Point (NICP) framework proposed by Amberg et al (2007) and based on the work of Allen et al (2003). NICP is the only method already used in the female breast shape domain to reconstruct a 3D breast model from a sequence of depth images (Lacher et al, 2019). Moreover, NICP was employed to construct the BFM and LSFM.…”

Section: Related Workmentioning

confidence: 99%

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

Weiherer,

Eigenberger,

Egger

et al. 2021

Preprint

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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, and the first ever publicly available. Together with the model, a fully automated, pairwise surface registration pipeline used to establish correspondence among 3D breast scans is introduced. Our method is computationally efficient and requires only four landmarks to guide the registration process. In order to weaken the strong coupling between breast and thorax, 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 for the RBSM. Ultimately, our model is seen as a first step towards combining physically motivated deformable models of the breast and statistical approaches in order to enable more realistic surgical outcome simulation.

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“…To satisfy the second constraint, many techniques have been developed. Among them, non-rigid registration methods allow to reconstruct patients' breasts with high precision, template-free, and markerless by solely using a low-cost depth camera (Lacher et al, 2017(Lacher et al, , 2019. The major drawback is the execution time ranging between 1 to 2 hours, making the method unpracticable for our application.…”

Section: Introductionmentioning

confidence: 99%

A rigged model of the breast for preoperative surgical planning

Mazier

Ribes

Gilles

et al. 2021

Journal of Biomechanics

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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.

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Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation

Cited by 14 publications

References 60 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

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

A rigged model of the breast for preoperative surgical planning

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