Novel radiographic presentation of primary syphilis of the tonsil

A 61-year-old HIV+ male presented to an infectious disease clinic with a complaint of sore throat. A painless ulcerated mass was discovered on the right tonsil resulting in further evaluation with a CT scan of the neck. Imaging confirmed the presence of a mass centered on the palatine tonsil and associated lymphadenopathy. A presumptive diagnosis of HPV-related squamous cell carcinoma was made due to patient risk factors. However, multiple biopsies found no evidence of carcinoma, but instead revealed the presence of spirochetes that stained positive fo

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource

Imputation of missing values for cochlear implant candidate audiometric data and potential applications.

ObjectiveAssess the real-world performance of popular imputation algorithms on cochlear implant (CI) candidate audiometric data.Methods7,451 audiograms from patients undergoing CI candidacy evaluation were pooled from 32 institutions with complete case analysis yielding 1,304 audiograms. Imputation model performance was assessed with nested cross-validation on randomly generated sparse datasets with various amounts of missing data, distributions of sparsity, and dataset sizes. A threshold for safe imputation was defined as root mean square error (RMSE) ResultsGreater quantities of missing data were associated with worse performance. Sparsity in audiometric data is not uniformly distributed, as inter-octave frequencies are less commonly tested. With 3-8 missing features per instance, a real-world sparsity distribution was associated with significantly better performance compared to other sparsity distributions (Δ RMSE 0.3 dB- 5.8 dB, non-overlapping 99% confidence intervals). With a real-world sparsity distribution, models were able to safely impute up to 6 missing datapoints in an 11-frequency audiogram. MICE consistently outperformed other models across all metrics and sparsity distributions (p ConclusionPrecision medicine will inevitably play an integral role in the future of hearing healthcare. These methods are data dependent, and rigorously validated imputation models are a key tool for maximizing datasets. Using the largest CI audiogram dataset to-date, we demonstrate that in a real-world scenario MICE can safely impute missing data for the vast majority (>99%) of audiograms with RMSE well below a clinically significant threshold of 10dB. Evaluation across a range of dataset sizes and sparsity distributions suggests a high degree of generalizability to future applications