Browsing by Subject "Microscopy"

Now showing 1 - 10 of 21
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    An Open Source Platform for Computational Histopathology
    (IEEE, 2021) Yu, Xiaxia; Zhao, Bingshuai; Huang, Haofan; Tian, Mu; Zhang, Sai; Song, Hongping; Li, Zengshan; Huang, Kun; Gao, Yi; Biostatistics and Health Data Science, School of Medicine
    Computational histopathology is a fast emerging field which converts the traditional glass slide based department to a new examination platform. Such a paradigm shift also brings the in silico computation to the field. Much research have been presented in the past decades on the algorithm development for pathology image analysis. On the other hand, a comprehensive software platform with advanced visualization and computation capability, large developer community, flexible plugin mechanism, and friendly transnational license, would be extremely beneficial for the entire community. In this work, we present SlicerScope: an open platform for whole slide histopathology image computing based on the highly successful 3D Slicer. We present rationale on the choice of such an architecture, introducing new modules/tools for giga-pixel whole slide image viewing, and four specific analytical modules for qualitative presentation, nucleus level analysis, tissue scale computation, and 3D pathology. The entire software is publicly available at https://slicerscope.github.io/ , facilitating the algorithmic, clinical, and transnational researches.
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    Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
    (Springer Nature, 2021) Bulten, Wouter; Balkenhol, Maschenka; Belinga, Jean-Joël Awoumou; Brilhante, Américo; Çakır, Aslı; Egevad, Lars; Eklund, Martin; Farré, Xavier; Geronatsiou, Katerina; Molinié, Vincent; Pereira, Guilherme; Roy, Paromita; Saile, Günter; Salles, Paulo; Schaafsma, Ewout; Tschui, Joëlle; Vos, Anne-Marie; ISUP Pathology Imagebase Expert Panel; van Boven, Hester; Vink, Robert; van der Laak, Jeroen; Hulsbergen-van der Kaa, Christina; Litjens, Geert; Pathology and Laboratory Medicine, School of Medicine
    The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.
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    Beta Cell Heterogeneity in the Acute Interferon Alpha Response
    (2025-06) Wagner, Leslie Elaine; Linnemann, Amelia; Elmendorf, Jeffrey; Roh, Hyun Cheol; Spaeth, Jason
    Type 1 diabetes (T1D) is characterized by autoimmune destruction of the insulin-producing β-cells in the pancreatic islet. This autoimmunity is hypothesized to result from a combination of genetic and environmental factors, with early childhood viral infection being a leading hypothesis for the latter. In response to viral infections, the innate immune system produces various cytokines, including interferon alpha (IFN-α), which has long been implicated in T1D pathogenesis. Our study explores how IFN-α influences human β-cell function, particularly its role in reactive oxygen species (ROS) production—signaling molecules essential for normal β-cell function but detrimental in excess. Using intravital microscopy and a β-cell-specific ROS biosensor, we identified a subset of β-cells that rapidly produce ROS in response to IFN-α. Interestingly, phenotyping data from the donors indicated that healthier β-cells were more likely to exhibit this response. In vitro experiments confirmed that IFN-α drives mitochondrial superoxide production in a subset human of β-cells, prompting us to investigate the molecular basis of this phenomenon. RNA sequencing of sorted IFN-α–treated cells revealed an upregulation of immune-related genes, and comparison with single-cell datasets showed that these genes are more highly expressed in β-cells from healthy individuals than those with T1D. These findings suggest that IFN-α–induced ROS production may be a marker of β-cell resilience, highlighting differences in how β-cells respond to stress. Understanding this mechanism could offer new insights into why some β-cells are more vulnerable in T1D and potentially point to novel strategies for preserving β-cell function in diabetes. While our investigation into IFN-α signaling revealed how immune cytokines influence β-cell physiology, we also sought to explore immune landscape changes during disease progression. Using the Akoya Phenocycler, we mapped immune cell populations in the pancreata of human and diabetic mouse models and corresponding controls, providing insight into disease-associated immune changes.
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    A comparison of a 2.26% fluoride varnish versus a 1.23% APF foam using polarized light microscopy, confocal microscopy and quantitative light fluorescence
    (2000) Quackenbush, Brett Michael; Dean, Jeffrey A.; Fontana, Margherita Ruth, 1966-; Stookey, George K.; Tomlin, Angela; Donly, Kevin J.
    Secondary caries and the replacement of existing restorations account for 50 to 70 percent of operative dentistry today. Quantitative Light Fluorescence (QLF) has been shown to be effective at diagnosing very early tooth demineralization on smooth surfaces (less than 50 μ in depth); however, QLF has never been utilized to evaluate secondary caries in dentin. The objective of this study was to validate the accuracy of QLF in diagnosing early secondary caries and then verify the results using confocal microscopy and polarized light microscopy. Seventy-five mandibular molar teeth were prepared with Class V amalgam preparations on the mesial surface. A fluoridated varnish and 1.23- percent acidulated phosphate fluoride (APF) were introduced to this evaluation system, two agents known to effectively inhibit tooth demineralization. The artificial caries system utilized was adjusted to ensure that secondary caries would occur at restoration/tooth surface interfaces. The teeth were exposed to this artificial caries challenge for five days and following lesion formation, QLF was used to determine if incipient demineralization could be detected. The results of the QLF analysis were then compared with the data gathered using confocal microscopy and polarized light microscopy. Our results demonstrate that QLF detected 100 percent of the lesions seen with confocal microscopy and polarized light microscopy; however, no sound specimens were analyzed with any of the three techniques. There were no consistent significant differences between the fluoridated varnish and APF (p < 0.05) with any of the three methods utilized. We conclude that QLF can be used in early caries diagnosis and that emphasis should now be focused on treatment of the early lesion.
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    Confocal Endomicroscopy Characteristics of Different Intraductal Papillary Mucinous Neoplasm Subtypes
    (2017-05) Kamboj, Amrit K; Dewitt, John M; Modi, Rohan M; Conwell, Darwin L; Krishna, Somashekar G; Medicine, School of Medicine
    Intraductal papillary mucinous neoplasms are classified into gastric, intestinal, pancreatobiliary, and oncocytic subtypes where morphology portends disease prognosis. The study aim was to demonstrate EUS-guided needle-based confocal laser endomicroscopy imaging features of intraductal papillary mucinous neoplasm subtypes. Four subjects, each with a specific intraductal papillary mucinous neoplasm subtype were enrolled. An EUS-guided needle-based confocal laser endomicroscopy miniprobe was utilized for image acquisition. The mean cyst size from the 4 subjects (2 females; mean age = 65.3±12 years) was 36.8±12 mm. All lesions demonstrated mural nodules and focal dilation of the main pancreatic duct. EUS-nCLE demonstrated characteristic finger-like papillae with inner vascular core for all subtypes. The image patterns of the papillae for the gastric, intestinal, and pancreatobiliary subtypes were similar. However, the papillae in the oncocytic subtype were thick and demonstrated a fine scale-like or honeycomb pattern with intraepithelial lumina correlating with histopathology. There was significant overlap in the needle-based confocal laser endomicroscopy findings for the different intraductal papillary mucinous neoplasm subtypes; however, the oncocytic subtype demonstrated distinct patterns. These findings need to be replicated in larger multicenter studies.
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    Convolutional neural network denoising in fluorescence lifetime imaging microscopy (FLIM)
    (SPIE, 2021) Mannam, Varun; Zhang, Yide; Yuan, Xiaotong; Hato, Takashi; Dagher, Pierre C.; Nichols, Evan L.; Smith, Cody J.; Dunn, Kenneth W.; Howard, Scott; Medicine, School of Medicine
    Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow processing speed, low signal- to-noise ratio (SNR), and expensive and challenging hardware setups. In this work, we demonstrate applying a denoising convolutional network to improve FLIM SNR. The network will integrated with an instant FLIM system with fast data acquisition based on analog signal processing, high SNR using high-efficiency pulse-modulation, and cost-effective implementation utilizing off-the-shelf radio-frequency components. Our instant FLIM system simultaneously provides the intensity, lifetime, and phasor plots in vivo and ex vivo. By integrating image de- noising using the trained deep learning model on the FLIM data, provide accurate FLIM phasor measurements are obtained. The enhanced phasor is then passed through the K-means clustering segmentation method, an unbiased and unsupervised machine learning technique to separate different fluorophores accurately. Our experimental in vivo mouse kidney results indicate that introducing the deep learning image denoising model before the segmentation effectively removes the noise in the phasor compared to existing methods and provides clearer segments. Hence, the proposed deep learning-based workflow provides fast and accurate automatic segmentation of fluorescence images using instant FLIM. The denoising operation is effective for the segmentation if the FLIM measurements are noisy. The clustering can effectively enhance the detection of biological structures of interest in biomedical imaging applications.
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    Deep learning-driven adaptive optics for single-molecule localization microscopy
    (Springer Nature, 2023) Zhang, Peiyi; Ma, Donghan; Cheng, Xi; Tsai, Andy P.; Tang, Yu; Gao, Hao-Cheng; Fang, Li; Bi, Cheng; Landreth, Gary E.; Chubykin, Alexander A.; Huang, Fang; Anatomy, Cell Biology and Physiology, School of Medicine
    The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM). Conventional sensorless adaptive optics methods rely on iterative mirror changes and image-quality metrics. However, these metrics result in inconsistent metric responses and thus fundamentally limit their efficacy for aberration correction in tissues. To bypass iterative trial-then-evaluate processes, we developed deep learning-driven adaptive optics for SMLM to allow direct inference of wavefront distortion and near real-time compensation. Our trained deep neural network monitors the individual emission patterns from single-molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter and drives a deformable mirror to compensate sample-induced aberrations. We demonstrated that our method simultaneously estimates and compensates 28 wavefront deformation shapes and improves the resolution and fidelity of three-dimensional SMLM through >130-µm-thick brain tissue specimens.
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    Dual-ligand fluorescence microscopy enables chronological and spatial histological assignment of distinct amyloid-β deposits
    (Elsevier, 2025) Klingstedt, Therése; Shirani, Hamid; Parvin, Farjana; Nyström, Sofie; Hammarström, Per; Graff, Caroline; Ingelsson, Martin; Vidal, Ruben; Ghetti, Bernardino; Sehlin, Dag; Syvänen, Stina; Nilsson, K. Peter. R.; Pathology and Laboratory Medicine, School of Medicine
    Different types of deposits comprised of amyloid-β (Aβ) peptides are one of the pathological hallmarks of Alzheimer's disease (AD) and novel methods that enable identification of a diversity of Aβ deposits during the AD continuum are essential for understanding the role of these aggregates during the pathogenesis. Herein, different combinations of five fluorescent thiophene-based ligands were used for detection of Aβ deposits in brain tissue sections from transgenic mouse models with aggregated Aβ pathology, as well as brain tissue sections from patients affected by sporadic or dominantly inherited AD. When analyzing the sections with fluorescence microscopy, distinct ligand staining patterns related to the transgenic mouse model or to the age of the mice were observed. Likewise, specific staining patterns of different Aβ deposits were revealed for sporadic versus dominantly inherited AD, as well as for distinct brain regions in sporadic AD. Thus, by using dual-staining protocols with multiple combinations of fluorescent ligands, a chronological and spatial histological designation of different Aβ deposits could be achieved. This study demonstrates the potential of our approach for resolving the role and presence of distinct Aβ aggregates during the AD continuum and pinpoints the necessity of using multiple ligands to obtain an accurate assignment of different Aβ deposits in the neuropathological evaluation of AD, as well as when evaluating therapeutic strategies targeting Aβ aggregates.
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    Editorial: Proceedings of the 2021 Indiana O'Brien Center Microscopy Workshop
    (Frontiers Media, 2022-05-02) Dunn, Kenneth W.; Hall, Andrew M.; Molitoris, Bruce A.; Medicine, School of Medicine
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    From pyramids to columns: the structure of the kidney
    (ASCP, 2012) Wood, Debra M.