Automated calculation of symmetry measure on clinical photographs

Objective Analysis of Breast Symmetry in Female Patients Undergoing Breast Reconstruction After Total Mastectomy

Background

Satisfaction with the breast aesthetic outcome is an expectation of breast reconstruction surgery, which is an integral part of cancer treatment for many patients. We evaluated postreconstruction breast symmetry in 82 female patients using distance and volume measurements.

Objectives

Clinical factors, such as reconstruction type (implant-based and autologous reconstruction), laterality, timing of reconstruction (immediate, delayed, and sequential), radiation therapy (RT), and demographic factors (age, BMI, race, and ethnicity), were evaluated as predictors of postoperative symmetry. Matched preoperative and postoperative measurements for a subset of 46 patients were used to assess correlation between preoperative and postoperative symmetry.

Methods

We used standardized differences between the left and right breasts for the sternal notch to lowest visible point distance and breast volume as metrics for breast, positional symmetry, and volume symmetry, respectively. We performed statistical tests to compare symmetry between subgroups of patients based on reconstruction type, laterality, timing, RT, and demographics.

Results

Overall, reconstruction type, reconstruction timing, and RT were observed to be factors significantly associated with postoperative symmetry, with implant reconstructions and immediate reconstruction procedures, and no RT showing better postoperative breast volume symmetry. Subgroup analyses, for both reconstruction type and laterality, showed superior volume symmetry for the bilateral implant reconstructions. No correlation was observed between preoperative and postoperative breast symmetry. Demographic factors were not significant predictors of postreconstruction symmetry.

Conclusions

This comprehensive analysis examines multiple clinical factors in a single study and will help both patients and surgeons make informed decisions about reconstruction options at their disposal.

Level of Evidence 3

Automated calculation of ptosis on lateral clinical photographs

RATIONALE, AIMS AND OBJECTIVES

The goal is to fully automate the calculation of a breast ptosis measure from clinical photographs through automatic localization of fiducial points relevant to the measure.

METHODS

Sixty-eight women (97 clinical photographs) who underwent or were scheduled for breast reconstruction were included. The photographs were divided into a development set (N = 49) and an evaluation set (N = 48). The breast ptosis measure is obtained automatically from distances between three fiducial points: the nipple, the lowest visible point of breast (LVP), and the lateral terminus of the inframammary fold (LT). The nipple is localized using the YIQ colour space to highlight the contrast between the areola and the surrounding breast skin. The areola is localized using its shape, location and high Q component intensity. The breast contour is estimated using Dijkstra's shortest path algorithm on the gradient of the photograph in greyscale. The lowest point of the estimated contour is set as the LVP. To locate the anatomically subtle LT, the location of patient's axilla is used as a reference.

RESULTS

The algorithm's efficacy was evaluated by comparing manual and automated localizations of the fiducial points. The average nipple diameter was used as a cut-off to define success. The algorithm showed 90, 91 and 83% accuracy for locating the nipple, LVP and LT in the evaluation set, respectively.

CONCLUSION

This study presents a new automated algorithm that may facilitate the quantification of breast ptosis from lateral views of patients' photographs.

Automated Identification of Fiducial Points on 3D Torso Images

Breast reconstruction is an important part of the breast cancer treatment process for many women. Recently, 2D and 3D images have been used by plastic surgeons for evaluating surgical outcomes. Distances between different fiducial points are frequently used as quantitative measures for characterizing breast morphology. Fiducial points can be directly marked on subjects for direct anthropometry, or can be manually marked on images. This paper introduces novel algorithms to automate the identification of fiducial points in 3D images. Automating the process will make measurements of breast morphology more reliable, reducing the inter- and intra-observer bias. Algorithms to identify three fiducial points, the nipples, sternal notch, and umbilicus, are described. The algorithms used for localization of these fiducial points are formulated using a combination of surface curvature and 2D color information. Comparison of the 3D co-ordinates of automatically detected fiducial points and those identified manually, and geodesic distances between the fiducial points are used to validate algorithm performance. The algorithms reliably identified the location of all three of the fiducial points. We dedicate this article to our late colleague and friend, Dr. Elisabeth K. Beahm. Elisabeth was both a talented plastic surgeon and physician-scientist; we deeply miss her insight and her fellowship.

A shape constrained parametric active contour model for breast contour detection

Quantitative measures of breast morphology can help a breast cancer survivor to understand outcomes of reconstructive surgeries. One bottleneck of quantifying breast morphology is that there are only a few reliable automation algorithms for detecting the breast contour. This study proposes a novel approach for detecting the breast contour, which is based on a parametric active contour model. In addition to employing the traditional parametric active contour model, the proposed approach enforces a mathematical shape constraint based on the catenary curve, which has been previously shown to capture the overall shape of the breast contour reliably. The mathematical shape constraint regulates the evolution of the active contour and helps the contour evolve towards the breast, while minimizing the undesired effects of other structures such as, the nipple/areola and scars. The efficacy of the proposed approach was evaluated on anterior posterior photographs of women who underwent or were scheduled for breast reconstruction surgery including autologous tissue reconstruction. The proposed algorithm shows promising results for detecting the breast contour.