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1. Machine Learning for Parkinson’s Disease Diagnosis Using Fundus Eye Images

   Diaz, Maximillian; Tian, Jianqiao; Fang, Ruogu (December 2020, Annual Meeting of Radiology Society of North America (RSNA))

   Purpose: Parkinson’s Disease (PD) is the second most common form of neural degeneration and defined by the decay of dopaminergic cells in the substantia nigra. The current standard for diagnosing PD occurs once 80% of dopaminergic cells have decayed. The degradation of these cells has been shown to create thinning of the retina walls and retina microvasculature. This work serves to find machine learning techniques to provide PD diagnosis using non-invasive fundus eye images. Materials and Methods: Two age and gender matched datasets where constructed using data from the UK Biobank (UKB) and data collected at the University of Florida (UF). The first dataset consists of 476 fundus eye images, 238 CN and 238 PD, sourced entirely from the UKB database. The second dataset, UF-UKB, consist of 100 images, 28 CN and 72 PD, collected at UF and 44 CN images from UKB. A second set of datasets, UKB-Green and UF-UKB-Green, were created using the green color channels to improve vessel segmentation. Vessel segmentation was performed using U-Net segmentation network. The vessel maps served as inputs to SVM classifying networks. Saliency maps were created to assess areas of interest for the networks. Results: The top performing SVM network for the UKB and UKB-Green datasets were the sigmoid SVM networks which achieved accuracies of .698 and .719 respectively. Meanwhile the top performing networks for the UF-UKB and UF-UKB-Green datasets where the linear SVM networks which achieved accuracies of .821 and .857 respectively. The saliency maps indicate that the different networks focused on different vessel structures with the most successful networks focusing more on smaller vessels. Conclusion: The results indicate that the machine learning networks can classify PD based on retina vasculature, with the key features being smaller blood vessels. The proposed methods further support the idea that changes in brain physiology can be observed in the eye. Machine learning networks can be applied to clinically available data and still provide accurate predictions Clinical Relevance statement, not to exceed 200 characters: The work illustrates the feasibility of utilizing eye images as a potential method for diagnosing PD, opposed to the current method of using motor symptoms. 

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2. Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis

   https://doi.org/10.34133/2022/9853606  

   Kaza, Nischita; Ojaghi, Ashkan; Robles, Francisco E. (January 2022, BME Frontiers)

   Objective and Impact Statement . We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. Introduction . Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining, segmentation, classification, and counting of white blood cells (WBCs) in single-channel images of peripheral blood smears. Methods . We train independent deep networks to virtually stain and segment grayscale images of smears. The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential. Results. Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining, the gold standard in hematology. The trained cellular and nuclear segmentation networks achieve high accuracy, and the classifier can achieve a quantitative five-part differential on unseen test data. Conclusion . This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple, fast, and low-cost, point-of-care hematology analyzer. 

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3. Computational framework for the generation of one‐dimensional vascular models accounting for uncertainty in networks extracted from medical images

   https://doi.org/10.1113/JP286193  

   Bartolo, Michelle_A; Taylor‐LaPole, Alyssa_M; Gandhi, Darsh; Johnson, Alexandria; Li, Yaqi; Slack, Emma; Stevens, Isaiah; Turner, Zachary_G; Weigand, Justin_D; Puelz, Charles; et al (July 2024, The Journal of Physiology)

   AbstractOne‐dimensional (1D) cardiovascular models offer a non‐invasive method to answer medical questions, including predictions of wave‐reflection, shear stress, functional flow reserve, vascular resistance and compliance. This model type can predict patient‐specific outcomes by solving 1D fluid dynamics equations in geometric networks extracted from medical images. However, the inherent uncertainty inin vivoimaging introduces variability in network size and vessel dimensions, affecting haemodynamic predictions. Understanding the influence of variation in image‐derived properties is essential to assess the fidelity of model predictions. Numerous programs exist to render three‐dimensional surfaces and construct vessel centrelines. Still, there is no exact way to generate vascular trees from the centrelines while accounting for uncertainty in data. This study introduces an innovative framework employing statistical change point analysis to generate labelled trees that encode vessel dimensions and their associated uncertainty from medical images. To test this framework, we explore the impact of uncertainty in 1D haemodynamic predictions in a systemic and pulmonary arterial network. Simulations explore haemodynamic variations resulting from changes in vessel dimensions and segmentation; the latter is achieved by analysing multiple segmentations of the same images. Results demonstrate the importance of accurately defining vessel radii and lengths when generating high‐fidelity patient‐specific haemodynamics models.image Key pointsThis study introduces novel algorithms for generating labelled directed trees from medical images, focusing on accurate junction node placement and radius extraction using change points to provide haemodynamic predictions with uncertainty within expected measurement error.Geometric features, such as vessel dimension (length and radius) and network size, significantly impact pressure and flow predictions in both pulmonary and aortic arterial networks.Standardizing networks to a consistent number of vessels is crucial for meaningful comparisons and decreases haemodynamic uncertainty.Change points are valuable to understanding structural transitions in vascular data, providing an automated and efficient way to detect shifts in vessel characteristics and ensure reliable extraction of representative vessel radii. 

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4. CNN-Modified Encoders in U-Net for Nuclei Segmentation and Quantification of Fluorescent Images

   https://doi.org/10.1109/ACCESS.2024.3436509  

   Shrestha, Aishwarya; Bao, Xuefeng; Cheng, Qingsu; Mcroy, Susan (January 2024, IEEE Access)

   This research introduces an advanced approach to automate the segmentation and quantification of nuclei in fluorescent images through deep learning techniques. Overcoming inherent challenges such as variations in pixel intensities, noisy boundaries, and overlapping edges, our devised pipeline integrates the U-Net architecture with state-of-the-art CNN models, such as EfficientNet. This fusion maintains the efficiency of U-Net while harnessing the superior capabilities of EfficientNet. Crucially, we exclusively utilize high-quality confocal images generated in-house for model training, purposefully avoiding the pitfalls associated with publicly available synthetic data of lower quality. Our training dataset encompasses over 3000 nuclei boundaries, which are meticulously annotated manually to ensure precision and accuracy in the learning process. Additionally, post-processing is implemented to refine segmentation results, providing morphological quantification for each segmented nucleus. Through comprehensive evaluation, our model achieves notable performance metrics, attaining an F1-score of 87% and an Intersection over Union (IoU) value of 80%. Furthermore, its robustness is demonstrated across diverse datasets sourced from various origins, indicative of its broad applicability in automating nucleus extraction and quantification from fluorescent images. This innovative methodology holds significant promise for advancing research efforts across multiple domains by facilitating a deeper understanding of underlying biological processes through automated analysis of fluorescent imagery. 

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5. Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin

   https://doi.org/10.1371/journal.pcbi.1008946  

   Praljak, Niksa; Iram, Shamreen; Goreke, Utku; Singh, Gundeep; Hill, Ailis; Gurkan, Umut A.; Hinczewski, Michael (November 2021, PLOS Computational Biology)

   Fadlelmola, Faisal Mohamed (Ed.)

   Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations—deformable and non-deformable sRBCs—utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k -folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k -folds, and matched trained personnel in overall characterization of whole channel images with R 2 = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring. 

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