Improved accuracy of breast volume calculation from 3D surface imaging data using statistical shape models

Göpper, Michael; Neubauer, Jakob; Kalash, Ziad; Stark, G. Björn; Šimunović, Filip

doi:10.1371/journal.pone.0233586

Cited by 10 publications

(6 citation statements)

References 33 publications

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“…Although values obtained by both methodologies still differ, the strong linear correlation coefficient suggests that 3D breast volume measurement using a low-cost surface scanning device, the Microsoft Kinect 1, is feasible and can approximate both the MRI breast volume and mastectomy specimen with sufficient accuracy. These results are aligned with previous reports, and the need for further developments is also conclusion reached by all authors [13,18,[20][21][22]. It is well accepted that the influence of the patient's position on volume calculation is an important limitation when correlating different volume calculations with different imaging modalities.…”

Section: Discussionsupporting

confidence: 89%

“…This means that a middle point between the anterior border of the latissimus dorsi muscle and the true external breast contour is very hard to determine, and is thus not a reproducible reference. Previous authors did not describe their methodology or differ on how to define breast boundaries applied to MRI segmentation [10,13,19,20]. The same applies to the inner breast limit and the superior pole limit.…”

Section: Discussionmentioning

confidence: 99%

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3D Breast Volume Estimation

et al. 2021

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Introduction: Breast volume estimation is considered crucial for breast cancer surgery planning. A single, easy, and reproducible method to estimate breast volume is not available. This study aims to evaluate, in patients proposed for mastectomy, the accuracy of the calculation of breast volume from a low-cost 3D surface scan (Microsoft Kinect) compared to the breast MRI and water displacement technique. Material and Methods: Patients with a Tis/T1–T3 breast cancer proposed for mastectomy between July 2015 and March 2017 were assessed for inclusion in the study. Breast volume calculations were performed using a 3D surface scan and the breast MRI and water displacement technique. Agreement between volumes obtained with both methods was assessed with the Spearman and Pearson correlation coefficients. Results: Eighteen patients with invasive breast cancer were included in the study and submitted to mastectomy. The level of agreement of the 3D breast volume compared to surgical specimens and breast MRI volumes was evaluated. For mastectomy specimen volume, an average (standard deviation) of 0.823 (0.027) and 0.875 (0.026) was obtained for the Pearson and Spearman correlations, respectively. With respect to MRI annotation, we obtained 0.828 (0.038) and 0.715 (0.018). Discussion: Although values obtained by both methodologies still differ, the strong linear correlation coefficient suggests that 3D breast volume measurement using a low-cost surface scan device is feasible and can approximate both the MRI breast volume and mastectomy specimen with sufficient accuracy. Conclusion: 3D breast volume measurement using a depth-sensor low-cost surface scan device is feasible and can parallel MRI breast and mastectomy specimen volumes with enough accuracy. Differences between methods need further development to reach clinical applicability. A possible approach could be the fusion of breast MRI and the 3D surface scan to harmonize anatomic limits and improve volume delimitation.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

3D Breast Volume Estimation

et al. 2021

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“…Besides the two exemplary applications shown in this paper, there is room for plenty of different applications, extensions, and improvements of our model. Inspired by recent work of Göpper et al [27], an interesting future application could include a thoracic wall into the RBSM to enable breast volume estimation from 3D surface scans. Furthermore, since our model is generative, it could be conveniently used for data augmentation in the machine learning domain.…”

Section: Discussionmentioning

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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“…Co. Ltd, Hangzhou, China). Firstly, reflective markers were placed around the breast border based on a validated breast volume model (Göpper et al, 2020). Participants…”

Section: Methodsmentioning

confidence: 99%

The effect of female breast surface area on heat‐activated sweat gland density and output

Blount,

Valenza,

Ward

et al. 2024

Experimental Physiology

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Female development includes significant morphological changes across the breast. Yet, whether differences in breast surface area (BrSA) modify sweat gland density and output remains unclear. The present study investigated the relationship between BrSA and sweat gland density and output in 22 young to middle‐aged women (2810 years) of varying breast sizes (BrSA range: 147–561 cm2) during a submaximal run in a warm environment (32 0.6°C; 53 1.7% relative humidity). Local sweat gland density and local sweat rate (LSR) above and below the nipple and at the bra triangle were measured. Expired gases were monitored for the estimation of evaporative requirements for heat balance (Ereq, in W/m2). Associations between BrSA and (i) sweat gland density; (ii) LSR; and (iii) sweat output per gland for the breast sites were determined via correlation and regression analyses. Our results indicated that breast sweat gland density decreased linearly as BrSA increased (r = −0.76, P < 0.001), whereas sweat output per gland remained constant irrespective of BrSA (r = 0.29, P = 0.28). This resulted in LSR decreasing linearly as BrSA increased (r = −0.62, P = 0.01). Compared to the bra triangle, the breast had a 64% lower sweat gland density (P < 0.001), 83% lower LSR (P < 0.001) and 53% lower output per gland (P < 0.001). BrSA (R2 = 0.33, P = 0.015) explained a greater proportion of variance in LSR than Ereq (in W/m2) (R2 = 0.07, P = 0.538). These novel findings extend the known relationship between body morphology and sweat gland density and LSR, to the female breast. This knowledge could innovate user‐centred design of sports bras by accommodating breast size‐specific needs for sweat management, skin wetness perception and comfort.

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Improved accuracy of breast volume calculation from 3D surface imaging data using statistical shape models

Cited by 10 publications

References 33 publications

3D Breast Volume Estimation

3D Breast Volume Estimation

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

The effect of female breast surface area on heat‐activated sweat gland density and output

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