Automated Identification of Fiducial Points on 3D Torso Images

Abstract

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.

Computation of breast ptosis from 3D surface scans of the female torso

“…For the LaTrenta and Hoffman classification, vertical distances were evaluated on 3D images using our customized software [15]. An observer manually recorded the nipple and IMF location and computed the vertical distance between nipple and IMF.…”

“…However, the IMF is often difficult to identify by photogrammetry or stereophotography because it is not only difficult to distinguish [15] but also occluded in patients with large breasts. We hypothesized that a method that minimizes the use of fiducial points would provide a more accurate quantitative assessment of breast ptosis than current methods and that 3D stereophotogrammetry offers such accuracy.…”

Automated spatial alignment of 3D torso images

“…The algorithm performs with a precision level of "precise" with a REM score of 2.8% (very good). The realignment allows uniform quantitative assessments to be performed and facilitates automated detection of fiducial points [6]. Future work, will compare the proposed method with existing 3D object alignment approaches, and evaluate the efficacy of automated fiducial point detection on the realigned images.…”

“…This orie quantitative assessments to be perfo implementation of algorithms for points, which rely on a forwa orientation of the torso [6]. Moreov robust and speedy processing, supp multiple images, and eliminates ope The flowchart in Figure 1B out automatic spatial alignment of 3D defined or desired spatial geometry.…”

Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

“…34 In fact, mean and Gaussian curvatures have been extensively used in the computer vision field for various tasks, including the automatic detection of a point of interest of some object. [34][35][36][37] In medical imaging, mean and Gaussian curvatures have been used to analyze the morphology of the brain cortex (i.e., gyrification) for various brain related disorders, including Autism and Alzheimer's disease. [31][32][33] In this respect, it is surprising that mean and Gaussian curvature values have no useful information for distinguishing malignant and benign breast lesions.…”