Browsing by Author "Badaoui, Hawraa"

Now showing 1 - 5 of 5
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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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    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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    Development of an integrated multimodal optical imaging system with real-time image analysis for the evaluation of oral premalignant lesions
    (SPIE, 2019) Yang, Eric C.; Vohra, Imran S.; Badaoui, Hawraa; Schwarz, Richard A.; Cherry, Katelin D.; Quang, Timothy; Jacob, Justin; Lang, Alex; Bass, Nancy; Rodriguez, Jessica; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Richards-Kortum, Rebecca R.; Bioengineering
    Oral premalignant lesions (OPLs), such as leukoplakia, are at risk of malignant transformation to oral cancer. Clinicians can elect to biopsy OPLs and assess them for dysplasia, a marker of increased risk. However, it is challenging to decide which OPLs need a biopsy and to select a biopsy site. We developed a multimodal optical imaging system (MMIS) that fully integrates the acquisition, display, and analysis of macroscopic white-light (WL), autofluorescence (AF), and high-resolution microendoscopy (HRME) images to noninvasively evaluate OPLs. WL and AF images identify suspicious regions with high sensitivity, which are explored at higher resolution with the HRME to improve specificity. Key features include a heat map that delineates suspicious regions according to AF images, and real-time image analysis algorithms that predict pathologic diagnosis at imaged sites. Representative examples from ongoing studies of the MMIS demonstrate its ability to identify high-grade dysplasia in OPLs that are not clinically suspicious, and to avoid unnecessary biopsies of benign OPLs that are clinically suspicious. The MMIS successfully integrates optical imaging approaches (WL, AF, and HRME) at multiple scales for the noninvasive evaluation of OPLs.
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    In Vivo Multimodal Optical Imaging: Improved Detection of Oral Dysplasia in Low-Risk Oral Mucosal Lesions
    (AACR, 2018) Yang, Eric C.; Schwarz, Richard A.; Lang, Alexander K.; Bass, Nancy; Badaoui, Hawraa; Vohra, Imran S.; Cherry, Katelin D.; Williams, Michelle D.; Gillenwater, Ann M.; Vigneswaran, Nadarajah; Richards-Kortum, Rebecca R.; Bioengineering
    Early detection of oral cancer and oral premalignant lesions (OPL) containing dysplasia could improve oral cancer outcomes. However, general dental practitioners have difficulty distinguishing dysplastic OPLs from confounder oral mucosal lesions in low-risk populations. We evaluated the ability of two optical imaging technologies, autofluorescence imaging (AFI) and high-resolution microendoscopy (HRME), to diagnose moderate dysplasia or worse (ModDys+) in 56 oral mucosal lesions in a low-risk patient population, using histopathology as the gold standard, and in 46 clinically normal sites. AFI correctly diagnosed 91% of ModDys+ lesions, 89% of clinically normal sites, and 33% of benign lesions. Benign lesions with severe inflammation were less likely to be correctly diagnosed by AFI (13%) than those without (42%). Multimodal imaging (AFI+HRME) had higher accuracy than either modality alone; 91% of ModDys+ lesions, 93% of clinically normal sites, and 64% of benign lesions were correctly diagnosed. Photos of the 56 lesions were evaluated by 28 dentists of varied training levels, including 26 dental residents. We compared the area under the receiver operator curve (AUC) of clinical impression alone to clinical impression plus AFI and clinical impression plus multimodal imaging using k-Nearest Neighbors models. The mean AUC of the dental residents was 0.71 (range: 0.45–0.86). The addition of AFI alone to clinical impression slightly lowered the mean AUC (0.68; range: 0.40–0.82), whereas the addition of multimodal imaging to clinical impression increased the mean AUC (0.79; range: 0.61–0.90). On the basis of these findings, multimodal imaging could improve the evaluation of oral mucosal lesions in community dental settings.
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    Multimodal optical imaging with real-time projection of cancer risk and biopsy guidance maps for early oral cancer diagnosis and treatment
    (SPIE, 2023) Coole, Jackson B.; Brenes, David R.; Mitbander, Ruchika; Vohra, Imran S.; Hou, Huayu; Kortum, Alex; Tang, Yubo; Maker, Yajur; Schwarz, Richard A.; Carns, Jennifer L.; Badaoui, Hawraa; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Richards-Kortum, Rebecca; Bioengineering
    Significance: Despite recent advances in multimodal optical imaging, oral imaging systems often do not provide real-time actionable guidance to the clinician who is making biopsy and treatment decisions. Aim: We demonstrate a low-cost, portable active biopsy guidance system (ABGS) that uses multimodal optical imaging with deep learning to directly project cancer risk and biopsy guidance maps onto oral mucosa in real time. Approach: Cancer risk maps are generated based on widefield autofluorescence images and projected onto the at-risk tissue using a digital light projector. Microendoscopy images are obtained from at-risk areas, and multimodal image data are used to calculate a biopsy guidance map, which is projected onto tissue.ResultsRepresentative patient examples highlight clinically actionable visualizations provided in real time during an imaging procedure. Results show multimodal imaging with cancer risk and biopsy guidance map projection offers a versatile, quantitative, and precise tool to guide biopsy site selection and improve early detection of oral cancers. Conclusions: The ABGS provides direct visible guidance to identify early lesions and locate appropriate sites to biopsy within those lesions. This represents an opportunity to translate multimodal imaging into real-time clinically actionable visualizations to help improve patient outcomes.