2D boundary shape detection based on camera for enhanced electrode placement in lung electrical impedance tomography

Accurate electrode placement is critical for improving image fidelity in lung Electrical Impedance Tomography (EIT), yet current systems rely on simplified circular templates that neglect patient-specific anatomical variation. This paper presents a novel, low-cost pipeline that uses smartphone-based photogrammetry to generate individualized 3D torso reconstructions for boundary-aligned electrode placement. The method includes automated video frame extraction, mesh post-processing, interactive 2D boundary extraction, real-world anatomical scaling, and both manual and automatic electrode detection. We evaluate two photogrammetry pipelines-commercial (RealityCapture) and open-source (Meshroom + MeshLab)-across five subjects including a mannequin and four human participants. Results demonstrate sub-centimeter Mean Absolute Error (MAE 0.42-0.60 cm) and Mean Percentage Error (MPE 8.56-11.51%) in electrode placement accuracy. Repeatability analysis shows good consistency with Coefficient of Variation below 15% for MPE and 19% for MAE. The generated subject-specific finite element meshes achieve 98.79% accuracy in cross-sectional area compared to direct measurements. While the current implementation requires 15-30 minutes processing time and multiple software tools, it establishes a foundation for more precise and personalized bioimpedance imaging that could benefit both clinical EIT and broader applications in neurological and industrial domains.