Abstract
Lens-free holographic imaging is important in many biomedical applications, as it offers a wider field of view, more mechanical robustness and lower cost than traditional microscopes. In many cases, it is important to be able to detect biological objects, such as blood cells, in microscopic images. However, state-of-the-art object detection methods are not designed to work on holographic images. Typically, the hologram must first be reconstructed into an image of the specimen, given a priori knowledge of the distance between the specimen and sensor, and standard object detection methods can then be used to detect objects in the reconstructed image. This paper describes a method for detecting objects directly in holograms while jointly reconstructing the image. This is achieved by assuming a sparse convolutional model for the objects being imaged and modeling the diffraction process responsible for generating the recorded hologram. This paper also describes an unsupervised method for training the convolutional templates, shows that the proposed method produces promising results for detecting white blood cells in holographic images, and demonstrates that the proposed object detection method is robust to errors in estimated focal depth.
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This work was funded by miDiagnostics.
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Yellin, F. et al. (2019). Joint Holographic Detection and Reconstruction. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_76
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DOI: https://doi.org/10.1007/978-3-030-32692-0_76
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