Publication

Abstract : Breast cancer related lymphedema (BCRL) is the swelling that generally occurs in the arms and is caused by the removal of or damage to lymph nodes as a part of invasive cancer treatment. Treating it at the earliest possible stage is the best way to manage the condition and prevent the pain, recurrent infection, reduced mobility, and impaired function. Current approaches for lymphedema detection include physical examination with tape measurement, lymphoscintigraphy, and magnetic resonance lymphangiography. The tape measurement method requires high manual involvement and lacks standardization, and the imaging modalities are expensive, time‐consuming and involve the injection of a contrast agent. To overcome these challenges, we built and validated a noncontact volumetric measurement system called rapid early screening for lymphedema (RESLy) consisting of (a) noninvasive Kinect infrared sensor‐based imaging that captures and builds a 3D reconstructed model of the body, (b) an automated segmentation process for extracting the region of interest (ROI), and (c) the volumetric computation of lymphedema. By employing density‐based spatial clustering of applications with noise (DBSCAN), which is a density‐based unsupervised learning algorithm, RESLy automatically segments out limbs from 3D maps, thereby streamlining automated measurements to be conducted quickly. The in‐lab calibration, testing, validation, and clinical deployment of RESLy among twelve patients in a tertiary cancer care department demonstrate that it is able to accurately identify limb volume differences with the least standard error of measurement, as well as identify lymphedema stages satisfactorily.