Low-cost surface reconstruction for aesthetic results assessment and prediction in breast cancer surgery

Abstract

The high incidence and low mortality of breast cancer surgery has led to an increasing emphasis on the cosmetic outcome of surgical treatment. Advances in aesthetic evaluation, as well as surgical planning and outcome prediction, have been investigated by using geometrically precise 3D modelling of the breast surface prior to surgery and after the procedure. However, existing solutions are based on expensive site specific setups and remain weakly validated. In this paper, we explore the possibility of using low-cost RGBD cameras as an affordable and mobile system for breast surface reconstruction. The methodology relies on sensor calibration, uncertainty-driven point filtering, dense reconstruction and subsequent multi-view joint optimization to diffuse residual pose errors. Results from a phantom study, with ground truth obtained through commercially available scanners, indicate that the approach is promising with RMS errors in order of 2 mm. A clinical study shows the practical applicability of our method and compares favourably to high-end scanning solutions.

ConoSurf: Open‐source 3D scanning system based on a conoscopic holography device for acquiring surgical surfaces

“…This similar value, in the presence of a larger spatial calibration error, could be a result of the real‐time visualization, which is helpful for filling in sparse regions of the acquired point‐set, consequently leading to more precise registrations. When comparing ConoSurf's point‐set registration results with those from studies in which they used a single‐shot device (such as time‐of‐flight cameras or structured light scanners) we find that their registration errors are in the same range as ours …”

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

“…‘Hold breath’ scanning protocols and respiratory gating techniques have been proposed to counter its effects (Patete et al., 2013). Among studies using single or two Kinect camera setups for breast surface imaging (Oliveira, Cardoso, Magalhães, Cardoso, 2014, Wheat, Choppin, Goyal, 2014, Costa, Monteiro, Zolfagharnasab, Oliveira, 2014, Pöhlmann, Hewes, Williamson, Sergeant, Hufton, Gandhi, Taylor, Astley, 2014, Lacher, Hipwell, Williams, Keshtgar, Hawkes, Stoyanov, 2015) only one produces complete denoised 3D breast surface models from a sequence of depth images (Lacher et al., 2015). Although discussing its apparent negative impact on reconstruction quality the work lacked a way of handling postural sway entirely.…”

A Comparative Study of Breast Surface Reconstruction for Aesthetic Outcome Assessment

“…Such systems rely upon proprietary software, whereas we only use freeware and open-source code. Kinect data sets are 3D reconstructed through three different state-of-the-art algorithms for RGBD data (ElasticFusion [15], InfiniTAM [11], Lacher et al [7]), along with an algorithm purely using RGB data (VisualSfM [16,3]). ElasticFusion features a joint geometric and photometric tracking component and time-windowed non-rigid loop closure strategies fusing data into a dense surfel cloud.…”