Ünlü, M. SelimManjunath, Shashank2022-06-232022-06-232022https://hdl.handle.net/2144/44776Single particle interferometric reflectance imaging sensor (SP-IRIS) allows for label- free biological nanoparticle detection. This imaging technique allows collection of a 3D defocus profile of interferometric signatures of particles on a reflecting surface. However, automated reconstruction of information from SP-IRIS data and identification of particles remains a challenge. In this work, we develop machine learning techniques to perform accurate reconstruction of defocus profiles from undersampled SP-IRIS data, and machine learning techniques to segment particles of interest from background in SP-IRIS data. We test the performance of a direct, single-signal fully connected neural network based re- construction technique, an untrained non-convolutional network for single-signal recon- struction, and a 2D convolutional neural network reconstruction model for reconstruction of SP-IRIS signal defocus profiles from undersampled data. We then test a UNet based segmentation model to segment particle signals from background signals. Lastly, we pro- pose a novel combined reconstruction and segmentation model which can perform both reconstruction and segmentation on undersampled SP-IRIS data.en-USAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Electrical engineeringThesis/Dissertation2022-05-240000-0003-3962-3689