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Genetic mouse models of skeletal abnormalities have demonstrated promise in the identification of phenotypes relevant to human skeletal diseases. Traditionally, phenotypes are assessed by manually examining radiographs, a tedious and potentially error-prone process. In response, this study developed a deep learning-based model that streamlines the measurement of murine bone lengths from radiographs in an accurate and reproducible manner. A bone detection and measurement pipeline utilizing the Keypoint R-CNN algorithm with an EfficientNet-B3 feature extraction backbone was developed to detect murine bone positions and measure their lengths. The pipeline was developed utilizing 94 X-ray images with expert annotations on the start and end position of each murine bone. The accuracy of our pipeline was evaluated on an independent dataset test with 592 images, and further validated on a previously published dataset of 21,300 mouse radiographs. The results showed that our model performed comparably to humans in measuring tibia and femur lengths (R2 > 0.92, p-value = 0) and significantly outperformed humans in measuring pelvic lengths in terms of precision and consistency. Furthermore, the model improved the precision and consistency of genetic association mapping results, identifying significant associations between genetic mutations and skeletal phenotypes with reduced variability. This study demonstrates the feasibility and efficiency of automated murine bone length measurement in the identification of mouse models of abnormal skeletal phenotypes. [ABSTRACT FROM AUTHOR]

Simple Summary: Oral cavity and pharyngeal cancer affects 560,000 people worldwide on a yearly basis. Despite advances in treatment, the survival rate remains poor, while prognoses typically improve with early diagnosis. The epithelium of the oral cavity often exhibits abnormal cellular growth, or dysplasia, that predisposes the patient to cancer depending on severity. Our study aims to address the current lack of rigorous quantitative methods for analyzing histopathological features relevant to clinical diagnosis, such as cellular morphology and epithelial layer number. We developed a deep learning approach that segments the oral epithelium and counts epithelial layer number within H&E-stained whole slide images. Our results demonstrate the feasibility of this automated approach for segmenting oral epithelium and counting its layer number. We also show its clinical relevance by comparing oral epithelium layer numbers between dysplasia of different severities. Head and neck squamous cell carcinoma (HNSCC), specifically in the oral cavity (oral squamous cell carcinoma, OSCC), is a common, complex cancer that significantly affects patients' quality of life. Early diagnosis typically improves prognoses yet relies on pathologist examination of histology images that exhibit high inter- and intra-observer variation. The advent of deep learning has automated this analysis, notably with object segmentation. However, techniques for automated oral dysplasia diagnosis have been limited to shape or cell stain information, without addressing the diagnostic potential in counting the number of cell layers in the oral epithelium. Our study attempts to address this gap by combining the existing U-Net and HD-Staining architectures for segmenting the oral epithelium and introducing a novel algorithm that we call Onion Peeling for counting the epithelium layer number. Experimental results show a close correlation between our algorithmic and expert manual layer counts, demonstrating the feasibility of automated layer counting. We also show the clinical relevance of oral epithelial layer number to grading oral dysplasia severity through survival analysis. Overall, our study shows that automated counting of oral epithelium layers can represent a potential addition to the digital pathology toolbox. Model generalizability and accuracy could be improved further with a larger training dataset. [ABSTRACT FROM AUTHOR]

Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF) imaging, which are not widely available in clinical settings due to high financial and time costs. To improve accessibility for clinical samples, we developed a computational algorithm to quantify hepatic ploidy using hematoxylin-eosin (H&E) histopathology images, which are commonly obtained during routine clinical practice. Our algorithm uses a deep learning model to first segment and classify different types of cell nuclei in H&E images. It then determines cellular ploidy based on the relative distance between identified hepatocyte nuclei and determines nuclear ploidy using a fitted Gaussian mixture model. The algorithm can establish the total number of hepatocytes and their detailed ploidy information in a region of interest (ROI) on H&E images. This is the first successful attempt to automate ploidy analysis on H&E images. Our algorithm is expected to serve as an important tool for studying the role of polyploidy in human liver disease. [ABSTRACT FROM AUTHOR]