A digital pathology tool for quantification of color features in histologic specimens
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ABSTRACT:
SUBMITTER: Reschke M
PROVIDER: S-EPMC8780932 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveProtective color restoration of faded digital pathology images based on color transfer algorithm.MethodsTwenty fresh tissue samples of invasive breast cancer from the pathology department of Qingdao Central Hospital in 2021 were screened. After HE staining, HE stained sections were irradiated with sunlight to simulate natural fading, and every 7 days was a fading cycle, and a total of 8 cycles were experienced. At the end of each cycle, the sections were digitally scanned to retain clear images, and the color changes of the sections during the fading process were recorded. The color transfer algorithm was applied to restore the color of the faded images; Adobe Lightroom Classic software presented the histogram of the image color distribution; UNet++ cell recognition segmentation model was used to identify the color restored images; Natural Image Quality Evaluator (NIQE), Information Entropy (Entropy), and Average Gradient (AG) were applied to evaluate the quality of the restored images.ResultsThe restored image color met the diagnostic needs of pathologists. Compared with the faded images, the NIQE value decreased (P<0.05), Entropy value increased (P<0.01), and AG value increased (P<0.01). The cell recognition rate of the restored image was significantly improved.ConclusionThe color transfer algorithm can effectively repair faded pathology images, restore the color contrast between nucleus and cytoplasm, improve the image quality, meet the diagnostic needs and improve the cell recognition rate of the deep learning model.
| S-EPMC10306179 | biostudies-literature
Project description:BackgroundEvaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence.MethodsThere were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis.ResultsMultiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836.ConclusionThese results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions.
| S-EPMC8628688 | biostudies-literature
Project description:Pulmonary fibrosis is characterized by abnormal interstitial extracellular matrix and cellular accumulations. Methods quantifying fibrosis severity in lung histopathology samples are semi-quantitative, subjective, and analyze only portions of sections. We sought to determine whether automated computerized imaging analysis shown to continuously measure fibrosis in mice could also be applied in human samples. A pilot study was conducted to analyze a small number of specimens from patients with Hermansky-Pudlak syndrome pulmonary fibrosis (HPSPF) or idiopathic pulmonary fibrosis (IPF). Digital images of entire lung histological serial sections stained with picrosirius red and alcian blue or anti-CD68 antibody were analyzed using dedicated software to automatically quantify fibrosis, collagen, and macrophage content. Automated fibrosis quantification based on parenchymal tissue density and fibrosis score measurements was compared to pulmonary function values or Ashcroft score. Automated fibrosis quantification of HPSPF lung explants was significantly higher than that of IPF lung explants or biopsies and was also significantly higher in IPF lung explants than in IPF biopsies. A high correlation coefficient was found between some automated quantification measurements and lung function values for the three sample groups. Automated quantification of collagen content in lung sections used for digital image analyses was similar in the three groups. CD68 immunolabeled cell measurements were significantly higher in HPSPF explants than in IPF biopsies. In conclusion, computerized image analysis provides access to accurate, reader-independent pulmonary fibrosis quantification in human histopathology samples. Fibrosis, collagen content, and immunostained cells can be automatically and individually quantified from serial sections. Robust automated digital image analysis of human lung samples enhances the available tools to quantify and study fibrotic lung disease.
| S-EPMC8240807 | biostudies-literature
Project description:The development of novel treatments for Progressive Supranuclear Palsy (PSP) is hindered by a knowledge gap of the impact of neurodegenerative neuropathology on brain structure and function. The current standard practice for measuring postmortem tau histology is semi-quantitative assessment, which is prone to inter-rater variability, time-consuming and difficult to scale. We developed and optimized a tau aggregate type-specific quantification pipeline for cortical and subcortical regions, in human brain donors with PSP. We quantified 4 tau objects ('neurofibrillary tangles', 'coiled bodies', 'tufted astrocytes', and 'tau fragments') using a probabilistic random forest machine learning classifier. The tau pipeline achieved high classification performance (F1-score > 0.90), comparable to neuropathologist inter-rater reliability in the held-out test set. Using 240 AT8 slides from 32 postmortem brains, the tau burden was correlated against the PSP pathology staging scheme using Spearman's rank correlation. We assessed whether clinical severity (PSP rating scale, PSPRS) score reflects neuropathological severity inferred from PSP stage and tau burden using Bayesian linear mixed regression. Tufted astrocyte density in cortical regions and coiled body density in subcortical regions showed the highest correlation to PSP stage (r = 0.62 and r = 0.38, respectively). Using traditional manual staging, only PSP patients in stage 6, not earlier stages, had significantly higher clinical severity than stage 2. Cortical tau density and neurofibrillary tangle density in subcortical regions correlated with clinical severity. Overall, our data indicate the potential for highly accurate digital tau aggregate type-specific quantification for neurodegenerative tauopathies; and the importance of studying tau aggregate type-specific burden in different brain regions as opposed to overall tau, to gain insights into the pathogenesis and progression of tauopathies.
| S-EPMC10634011 | biostudies-literature
Project description:Mitosis is a critical criterion for meningioma grading. However, pathologists' assessment of mitoses is subject to significant inter-observer variation due to challenges in locating mitosis hotspots and accurately detecting mitotic figures. To address this issue, we leverage digital pathology and propose a computational strategy to enhance pathologists' mitosis assessment. The strategy has two components: (1) A depth-first search algorithm that quantifies the mathematically maximum mitotic count in 10 consecutive high-power fields, which can enhance the preciseness, especially in cases with borderline mitotic count. (2) Implementing a collaborative sphere to group a set of pathologists to detect mitoses under each high-power field, which can mitigate subjective random errors in mitosis detection originating from individual detection errors. By depth-first search algorithm (1) , we analyzed 19 meningioma slides and discovered that the proposed algorithm upgraded two borderline cases verified at consensus conferences. This improvement is attributed to the algorithm's ability to quantify the mitotic count more comprehensively compared to other conventional methods of counting mitoses. In implementing a collaborative sphere (2) , we evaluated the correctness of mitosis detection from grouped pathologists and/or pathology residents, where each member of the group annotated a set of 48 high-power field images for mitotic figures independently. We report that groups with sizes of three can achieve an average precision of 0.897 and sensitivity of 0.699 in mitosis detection, which is higher than an average pathologist in this study (precision: 0.750, sensitivity: 0.667). The proposed computational strategy can be integrated with artificial intelligence workflow, which envisions the future of achieving a rapid and robust mitosis assessment by interactive assisting algorithms that can ultimately benefit patient management.
| S-EPMC10782692 | biostudies-literature
Project description:PURPOSE:Digital pathology (DP), referring to the digitization of tissue slides, is beginning to change the landscape of clinical diagnostic workflows and has engendered active research within the area of computational pathology. One of the challenges in DP is the presence of artefacts and batch effects, unintentionally introduced during both routine slide preparation (eg, staining, tissue folding) and digitization (eg, blurriness, variations in contrast and hue). Manual review of glass and digital slides is laborious, qualitative, and subject to intra- and inter-reader variability. Therefore, there is a critical need for a reproducible automated approach of precisely localizing artefacts to identify slides that need to be reproduced or regions that should be avoided during computational analysis. METHODS:Here we present HistoQC, a tool for rapidly performing quality control to not only identify and delineate artefacts but also discover cohort-level outliers (eg, slides stained darker or lighter than others in the cohort). This open-source tool employs a combination of image metrics (eg, color histograms, brightness, contrast), features (eg, edge detectors), and supervised classifiers (eg, pen detection) to identify artefact-free regions on digitized slides. These regions and metrics are presented to the user via an interactive graphical user interface, facilitating artefact detection through real-time visualization and filtering. These same metrics afford users the opportunity to explicitly define acceptable tolerances for their workflows. RESULTS:The output of HistoQC on 450 slides from The Cancer Genome Atlas was reviewed by two pathologists and found to be suitable for computational analysis more than 95% of the time. CONCLUSION:These results suggest that HistoQC could provide an automated, quantifiable, quality control process for identifying artefacts and measuring slide quality, in turn helping to improve both the repeatability and robustness of DP workflows.
| S-EPMC6552675 | biostudies-literature
Project description:Previous studies have successfully identified genetic variants in several genes associated with human iris (eye) color; however, they all used simplified categorical trait information. Here, we quantified continuous eye color variation into hue and saturation values using high-resolution digital full-eye photographs and conducted a genome-wide association study on 5,951 Dutch Europeans from the Rotterdam Study. Three new regions, 1q42.3, 17q25.3, and 21q22.13, were highlighted meeting the criterion for genome-wide statistically significant association. The latter two loci were replicated in 2,261 individuals from the UK and in 1,282 from Australia. The LYST gene at 1q42.3 and the DSCR9 gene at 21q22.13 serve as promising functional candidates. A model for predicting quantitative eye colors explained over 50% of trait variance in the Rotterdam Study. Over all our data exemplify that fine phenotyping is a useful strategy for finding genes involved in human complex traits.
| S-EPMC2865509 | biostudies-literature
Project description:To clarify the significance of quantitative analyses of amyloid proteins in clinical practice and in research relating to systemic amyloidoses, we applied mass spectrometry-based quantification by isotope-labeled cell-free products (MS-QBIC) to formalin-fixed, paraffin-embedded (FFPE) tissues. The technique was applied to amyloid tissues collected by laser microdissection of Congo red-stained lesions of FFPE specimens. Twelve of 13 amyloid precursor proteins were successfully quantified, including serum amyloid A (SAA), transthyretin (TTR), immunoglobulin kappa light chain (IGK), immunoglobulin lambda light chain (IGL), beta-2-microglobulin (B2M), apolipoprotein (Apo) A1, Apo A4, Apo E, lysozyme, Apo A2, gelsolin, and fibrinogen alpha chain; leukocyte cell-derived chemotaxin-2 was not detected. The quantification of SAA, TTR, IGK, IGL, and B2M confirmed the responsible proteins, even when the immunohistochemical results were not decisive. Considerable amounts of Apo A1, Apo A4, and Apo E were deposited in parallel amounts with the responsible proteins. Quantification of amyloid protein by MS-QBIC is feasible and useful for the classification of and research on systemic amyloidoses.
| S-EPMC7329117 | biostudies-literature
Project description:Pathology is on the verge of a profound change from an analog and qualitative to a digital and quantitative discipline. This change is mostly driven by the high-throughput scanning of microscope slides in modern pathology departments, reaching tens of thousands of digital slides per month. The resulting vast digital archives form the basis of clinical use in digital pathology and allow large scale machine learning in computational pathology. One of the most crucial bottlenecks of high-throughput scanning is quality control (QC). Currently, digital slides are screened manually to detected out-of-focus regions, to compensate for the limitations of scanner software. We present a solution to this problem by introducing a benchmark dataset for blur detection, an in-depth comparison of state-of-the art sharpness descriptors and their prediction performance within a random forest framework. Furthermore, we show that convolution neural networks, like residual networks, can be used to train blur detectors from scratch. We thoroughly evaluate the accuracy of feature based and deep learning based approaches for sharpness classification (99.74% accuracy) and regression (MSE 0.004) and additionally compare them to domain experts in a comprehensive human perception study. Our pipeline outputs spacial heatmaps enabling to quantify and localize blurred areas on a slide. Finally, we tested the proposed framework in the clinical setting and demonstrate superior performance over the state-of-the-art QC pipeline comprising commercial software and human expert inspection by reducing the error rate from 17% to 4.7%.
| S-EPMC9113532 | biostudies-literature
Project description:The persistence of covalently closed circular DNA (cccDNA) poses a major obstacle to curing chronic hepatitis B (CHB). Here, we used droplet digital PCR (ddPCR) for cccDNA quantitation. The cccDNA-specific ddPCR showed high accuracy with the dynamic range of cccDNA detection from 101 to 105 copies/assay. The ddPCR had higher sensitivity, specificity and precisely than qPCR. The results of ddPCR correlated closely with serum HB core-related antigen and HB surface antigen (HBsAg) in 24 HBV-infected human-liver-chimeric mice (PXB-mice). We demonstrated that in 2 PXB-mice after entecavir treatment, the total cccDNA content did not change during liver repopulation, although the cccDNA content per hepatocyte was reduced after the treatment. In the 6 patients with HBV-related hepatocellular carcinoma, ddPCR detected cccDNA in both tumor and non-tumor tissues. In 13 HBeAg-negative CHB patients with pegylated interferon alpha-2a, cccDNA contents from paired biopsies were more significantly reduced in virological response (VR) than in non-VR at week 48 (p = 0.0051). Interestingly, cccDNA levels were the lowest in VR with HBsAg clearance but remained detectable after the treatment. Collectively, ddPCR revealed that cccDNA content is stable during hepatocyte proliferation and persists at quantifiable levels, even after serum HBsAg clearance.
| S-EPMC8826402 | biostudies-literature