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Parametric Representation of Human Breast Shapes
Abstract
This paper proposes a novel technique to describe the shapes of women breasts in a low dimensional parameter space. The parameterization is obtained applying Principal Component Analysis (PCA) to a data set of about 40 Nuclear Magnetic Resonances of female breasts taken in a prone position. The resulting principal modes may be used as clinical indicator and have a direct medical interpretation.
... We demonstrated in our previous experience that curvature could be a good descriptor of breast shape. 26 We can associate curvature with common qualitative estimates such as ptosis or volume (a very curved breast is usually large and ptotic, whereas a flat one can be very small and without ptosis). However, although curvature is a local property that can be estimated in a defined region of the breast, a single value cannot describe the entire surface. ...
Background
Breast shape is defined utilizing mainly qualitative assessment (full, flat, ptotic) or estimates, such as volume or distances between reference points, that cannot describe it reliably.
Objectives
We will quantitatively describe breast shape with two parameters derived from a statistical methodology denominated principal component analysis (PCA).
Methods
We created a heterogeneous dataset of breast shapes acquired with a commercial infrared 3-dimensional scanner on which PCA was performed. We plotted on a Cartesian plane the two highest values of PCA for each breast (principal components 1 and 2). Testing of the methodology on a preoperative and postoperative surgical case and test-retest was performed by two operators.
Results
The first two principal components derived from PCA are able to characterize the shape of the breast included in the dataset. The test-retest demonstrated that different operators are able to obtain very similar values of PCA. The system is also able to identify major changes in the preoperative and postoperative stages of a two-stage reconstruction. Even minor changes were correctly detected by the system.
Conclusions
This methodology can reliably describe the shape of a breast. An expert operator and a newly trained operator can reach similar results in a test/re-testing validation. Once developed and after further validation, this methodology could be employed as a good tool for outcome evaluation, auditing, and benchmarking.
... Thus, the model parameters should be easily personalized with respect to patient and tumor characterizations during the consultations. Alternative strategies to model breast deformations encompass the fitting of parametric models [26][27][28][29][30], physical equations to describe known breast deformations [31], user-intuitive parameters to change breast shape [32,33] or dataset of known cases to simulate breast surgery outcomes [34]. These strategies model the breast with limited number of parameters and produce results in a timely manner more adequate to clinical practice, but the modelling of breast deformations has still to be improved for clinical surgery planning applications. ...
Breast cancer treatments can have a negative impact on breast aesthetics, in case when surgery is intended to intersect tumor. For many years mastectomy was the only surgical option, but more recently breast conserving surgery (BCS) has been promoted as a liable alternative to treat cancer while preserving most part of the breast. However, there is still a significant number of BCS intervened patients who are unpleasant with the result of the treatment, which leads to self-image issues and emotional overloads. Surgeons recognize the value of a tool to predict the breast shape after BCS to facilitate surgeon/patient communication and allow more educated decisions; however, no such tool is available that is suited for clinical usage. These tools could serve as a way of visually sensing the aesthetic consequences of the treatment. In this research, it is intended to propose a methodology for predict the deformation after BCS by using machine learning techniques. Nonetheless, there is no appropriate dataset containing breast data before and after surgery in order to train a learning model. Therefore, an in-house semi-synthetic dataset is proposed to fulfill the requirement of this research. Using the proposed dataset, several learning methodologies were investigated, and promising outcomes are obtained.
... It presents better fitting results in comparison with the physic-based approach of [5]. Other works [7,8] propose a parametrization of the breast by applying Principal Component Analysis (PCA) to datasets of Nuclear Magnetic Resonances. Breast models are generated by manipulating principal components to obtain the desired breast shape. ...
The development of a three-dimensional (3D) planing tool for breast cancer surgery requires the existence of proper deformable models of the breast, with parameters that can be manipulated to obtain the desired shape. However, modelling breast is a challenging task due to the lack of physical landmarks that remain unchanged after deformation. In this paper, the fitting of a 3D point cloud of the breast to a parametric model suitable for surgery planning is investigated. Regression techniques were used to learn breast deformation functions from exemplar data, resulting in comprehensive models easy to manipulate by surgeons. New breast shapes are modelled by varying the type and degree of deformation of three common deformations: ptosis, turn and top-shape.
... All this time, the standard measurement used in body contouring have referred to Caucasian parameters which may not be applicable [12][13][14][15] In body contouring, the preoperative planning includes predicted body measurement and operative design is very important. Three dimension (3D) scan has been used to assess preoperative and postoperative body conditions in some centers, but it cannot be used as a model for making quantitative operative planning [20][21]. ...
Plastic reconstructive and aesthetic surgery has emerged to meet the challenge of improving body size and shape. Currently, body reconstruction in Indonesia refers to Caucasian parameters which may not be applicable. Moreover, patients cannot comprehend the surgeon’s prediction of the surgical outcome visually making it more difficult for the surgeon to describe the operative result. Reseach Objective is understanding the supernormal body concept in Indonesia, describing variation of normal and supernormal body indexes of Indonesians as a formulation base and to make a 3D digital modeling of normal and supernormal person. These models will be used as guidance for planning and predicting the reconstruction - aesthetic surgery in body contouring and as assisting tool for giving information visually to patients. Reseach method using applied study. Questionnaires were distributed to a hundred male and female adults respondent to obtain the concept of Indonesian normal and supernormal bodies. The Body model are 17-25 years old and then divided into 2 groups, normal and supernormal by expert juries assessment. The data of body index anthropometry were presented in tables, charts, and narration to describe normal and supernormal morphologic variations, then results between the 2 groups were compared statistically by t-test and discriminant test. Anthropometric and photographic data were used for making normal and supernormal 3D digital woman models. Criteria of body attractiveness by questionnaire were bright skin color, tallness, slimness, hour glass body shape, muscular arms, medium sized shoulders, hip, waist, breasts and buttocks, wide chest and slender legs. There were significant differences in body measurement between normal-supernormal and distinguishing parameters between normal -supernormal groups by discriminant test including chest dimension index, waist hip ratio, triceps skinfold and leg length. There were two 3D digital woman models for both normal and supernormal subjects which had measurements approximating the average values of each group.
Breast cancer is one of the most mediated malignant diseases, because of its high incidence and prevalence, but principally due to its physical and psychological invasiveness. Surgeons and patients have often many options to consider for undergoing the procedure. The ability to visualise the potential outcomes of the surgery and make decisions on their surgical options is, therefore, very important for patients and surgeons. In this paper we investigate the fitting of a 3d point cloud of the breast to a parametric model usable in surgery planning, obtaining very promising results with real data.
In this paper, we present the modeling and estimation aspects of a virtual reality system for plastic and reconstructive breast surgery. Our system has two modes, a model creation mode and a model fitting mode. The model creation mode allows a surgeon to interactively adjust the shape of a virtual breast by varying key shape variables, analogous to the aesthetic and structural elements surgeons inherently vary manually during breast reconstruction. Our contribution is a set of global deformations with very intuitive parameters that a surgeon can apply to a generic geometric primitive in order to model the breast of his/her patient for pre-operative planning purposes and for communicating this plan to the patient. The model fitting mode allows the system to automatically fit a generic deformable model to patient specific three-dimensional breast surface measurements using a physically-based framework. We have tested the accuracy of our technique using both synthetic and real input data with very encouraging results.
We develop a novel method for fitting high-resolution template meshes to detailed human body range scans with sparse 3D markers. We formulate an optimization problem in which the degrees of freedom are an affine transformation at each template vertex. The objective function is a weighted combination of three measures: proximity of transformed vertices to the range data, similarity between neighboring transformations, and proximity of sparse markers at corresponding locations on the template and target surface. We solve for the transformations with a non-linear optimizer, run at two resolutions to speed convergence. We demonstrate reconstruction and consistent parameterization of 250 human body models. With this parameterized set, we explore a variety of applications for human body modeling, including: morphing, texture transfer, statistical analysis of shape, model fitting from sparse markers, feature analysis to modify multiple correlated parameters (such as the weight and height of an individual), and transfer of surface detail and animation controls from a template to fitted models.
It has been difficult to obtain accurate surface data for women's breasts, since the borderline of the breast is ambiguous at the skin surface. In this paper, we scanned 37 women's nude breasts using 3D phase shifting moiré and explored a measurement protocol to get a reliable boundary of the breast and thereby provided new shape parameters for breasts. The folding line method was useful for finding a continuous and natural boundary for the breasts, so that the breasts base and volume could be measured more accurately. The global average radius of the curvature of the bottom breast line was suggested as a useful shape parameter for the design of a comfortable and form-fitting brassiere, especially for those who have a skewed bottom breast curve. It is also expected that data on breast volume based on natural anatomy would be a useful reference for surgical operation on the breast.
Outcome evaluation in cosmetic and reconstructive surgery of the breast is commonly performed visually or employing bi-dimensional photography. The reconstructive process in the era of anatomical implants requires excellent survey capabilities that mainly rely on surgeon experience. In this paper we present a set of parameters to unambiguously estimate the shape of natural and reconstructed breast. A digital laser scanner was employed on seven female volunteers. A graphic depiction of curvature of the thoracic surface has been the most interesting result. Further work is required to provide clinical and instrumental validation to our technique.
The CAESAR study (Civilian American and European Surface
Anthropometry Resource) is a survey of body measurements for people ages
18-65 in three countries: USA, The Netherlands, and Italy. It is a
collaborative effort with partners from several countries and many
industries. It employs the latest in 3D measurement technology, and is
the first 3D surface anthropometry survey of the US and Europe. Data
from the study will allow us to effectively design everything from
better fitting clothes and protective equipment to better seats and
workstations, as well as dramatically reducing the cost to design and
manufacture them
Breast shape analysis on three-dimensional models
- G Catanuto
- G Gallo
- G M Farinella
- G Impoco
- M B Nava
- A Pennati
- A Spano
Modeling for Plastic and Reconstructive Breast Surgery,Medical Image Computing and Computer-Assisted Intervention
- D T Chen
- I A Kakadiaris
- M J Miller
- R B Loftin
- C Patrick