PS $^{2}$ F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing

Ghanekar, Bhargav; Saragadam, Vishwanath; Mehra, Dushyant; Gustavsson, Anna-Karin; Sankaranarayanan, Aswin C.; Veeraraghavan, Ashok

doi:10.1109/tpami.2022.3202511

Cited by 5 publications

(2 citation statements)

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“…These measure four linear polarization channels and provide a new avenue for snapshot multi-coded imaging. For example, the recent work of Ghanekar et al [23] uses two of the four polarization channels with a task-specific, polarization-dependent spatial modulator for snapshot depth imaging. In our work, we aim to expand the capabilities and potential of multi-coded imaging with polarization.…”

Section: Introductionmentioning

confidence: 99%

Compact Incoherent Spatial Frequency Filtering Enabled by Metasurface Engineering

Hazineh

Guo

Shi

et al. 2021

Conference on Lasers and Electro-Optics

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Section: Introductionmentioning

confidence: 99%

Compact Incoherent Spatial Frequency Filtering Enabled by Metasurface Engineering

Hazineh

Guo

Shi

et al. 2021

Conference on Lasers and Electro-Optics

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“…Computational imaging and especially end-to-end optimized optics, sensors and algorithms have emerged as a powerful tool for achieving performance beyond that of conventional optics. For example, single-shot 3D microscopy [19][20][21][22] , extended depth-of-field microscope 23 , and lensless microscopes [24][25][26][27] have shown great potential for high-resolution imaging using simple and compact systems. The key ingredient in all these techniques is that co-designing optics and algorithms allows these systems to overcome the limits of conventional optics.…”

mentioning

confidence: 99%

DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology

Jin,

Tang,

Coole

et al. 2024

Nat Commun

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Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.

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