Browsing by Author "Jin, Lingbo"

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    Deep learning extended depth-of-field microscope for fast and slide-free histology
    (PNAS, 2020) Jin, Lingbo; Tang, Yubo; Wu, Yicheng; Coole, Jackson B.; Tan, Melody T.; Zhao, Xuan; Badaoui, Hawraa; Robinson, Jacob T.; Williams, Michelle D.; Gillenwater, Ann M.; Richards-Kortum, Rebecca R.; Veeraraghavan, Ashok; Bioengineering; Electrical and Computer Engineering
    Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells—a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 µm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings.
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    Deep-learning-enabled computational microscopy for rapid cancer detection
    (2024-01-16) Jin, Lingbo; Veeraraghavan, Ashok
    The gold standard for cancer detection in diagnosis and treatment guidance is based on histopathology, the examination of cells under a microscope. However, the tissue in question will need to be removed from the patient, sectioned into very thin slices, and stained before it can be examined by a pathologist. This preparation process is time-consuming and labor-intensive. The first part of this thesis focuses on an ex vivo ultraviolet extended depth-of-field microscope that can rapidly image large areas of freshly resected tissue, providing histologic quality images without physical sectioning. To overcome challenges in directly imaging thick intact tissue, such as subsurface scattering, high tissue surface irregularities, and difficulties in histology interpretation, the proposed microscopy platform unifies UV surface excitation, end-to-end extended depth-of-field, and GAN-based virtual staining into a single, coherent pipeline. This microscope provides an inexpensive and easy-to-use alternative to standard histopathology. The second part of this thesis extends the capabilities of an in vivo high-resolution micro-endoscopy (HRME) by enabling 3D volume imaging. The HRME is a minimally invasive microscope with an imaging fiber that can be used alongside an endoscope and provides cellular-resolution histological images in real-time. However, the micro-endoscopy can only image the superficial layer of the tissue in 2D. By incorporating a custom-designed phase mask with the fiber, the new Mask-HRME system can perform volumetric imaging with variable focusing ability. This micro-endoscopy will enable physicians to look deeper into the tissue without performing more invasive procedures. In both of the projects, computational imaging methods that jointly design the optics and image processing algorithm enables these new microscopes to break the limit of conventional microscopy and capture more diagnostic information than was previously possible.
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    DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology
    (Springer Nature, 2024) Jin, Lingbo; Tang, Yubo; Coole, Jackson B.; Tan, Melody T.; Zhao, Xuan; Badaoui, Hawraa; Robinson, Jacob T.; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Richards-Kortum, Rebecca R.; Veeraraghavan, Ashok; Bioengineering; Electrical and Computer Engineering
    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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    DeepDOF: Deep learning extended depth-of-field microscope for fast and slide-free histology of surgical specimens
    (2020-10-22) Jin, Lingbo; Veeraraghavan, Ashok
    Histopathology is the gold standard for cancer diagnosis, but histopathology slide preparation is expensive, time- and labor-intensive. Slide-free pathology with fluo- rescence microscopy could offer a faster and less costly alternative. However, rapidly imaging intact tissue with surface irregularities (∼ 200μm) is fundamentally con- strained by the intrinsic trade-off between resolution and depth-of-field (DOF). In this study, we present a novel computational microscope that can image intact spec- imens with cellular resolution without re-focusing. The system is designed to pro- vide real-time deep learning based histopathology of intact specimen using extended depth-of-field (DeepDOF). the DeepDOF microscope consists of a conventional microscope with the addition of a wavefront-encoding phase mask and a neural network that jointly extends the DOF while maintaining subcellular resolution. Leveraging advances in deep learning, we simultaneously designed and optimized the two key components in the DeepDOF network end-to-end. First, the optical layer simulates the phase mask that creates a depth-dependent and invertible point spread function (PSF). These PSF sections can encode surface texture/intensity information regardless of the surface topology. Sec- ond, an artificial intelligence-based digital layer is used to deconvolve and extract high resolution image information from the captured data. In this study, we trained the DeepDOF network and optimized the microscope design with a large image dataset consisting of varied imaging features from human histology to natural scenes. The optimized phase mask was then fabricated using reactive ion etching and inserted into the aperture plane of a 4x, 0.13 NA epi-fluorescence microscope, which was further integrated with an automated x-y sample stage for tissue mapping. We calibrated the depth dependent PSFs of the DeepDOF microscope using 1 μm fluorescent beads. By imaging resolution target, we show that the DeepDOF microscope can con- sistently resolve subcellular features within a 200 μm depth-of-field, thus allowing the visualization of nuclear morphology on highly irregular tissue surfaces without serial focusing. We validated DeepDOF microscope’s performance by imaging freshly resected and proflavine-stained porcine esophageal tissue and human oral tissue. Fur- thermore, we show that DeepDOF images reveal a variety of important diagnostic features confirmed by standard histopathology. In the long term, the DeepDOF mi- croscope can substantially contribute to histopathological assessment of intact biop- sies and surgical specimens, especially for intraoperative evaluation and in resource- constrained settings.