CVJan 16, 2019

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

arXiv:1901.05377v113 citations
Originality Incremental advance
AI Analysis

This addresses the need for affordable and accurate 3D breast modeling in breast cancer treatment, offering a practical alternative to expensive scanners, though it is incremental as it builds on existing registration techniques.

The paper tackled the problem of creating clinical-quality 3D breast surface models for surgical planning and aesthetic evaluation by using a low-cost RGBD camera with a non-rigid registration method, achieving better reconstructions with lower landmark errors and more accurate volume estimates compared to previous approaches.

Accounting for 26% of all new cancer cases worldwide, breast cancer remains the most common form of cancer in women. Although early breast cancer has a favourable long-term prognosis, roughly a third of patients suffer from a suboptimal aesthetic outcome despite breast conserving cancer treatment. Clinical-quality 3D modelling of the breast surface therefore assumes an increasingly important role in advancing treatment planning, prediction and evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive and either infrastructure-heavy or subject to motion artefacts. In this paper we employ a single consumer-grade RGBD camera with an ICP-based registration approach to jointly align all points from a sequence of depth images non-rigidly. Subtle body deformation due to postural sway and respiration is successfully mitigated leading to a higher geometric accuracy through regularised locally affine transformations. We present results from 6 clinical cases where our method compares well with the gold standard and outperforms a previous approach. We show that our method produces better reconstructions qualitatively by visual assessment and quantitatively by consistently obtaining lower landmark error scores and yielding more accurate breast volume estimates.

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