Machine learning based prediction of squamous cell carcinoma in ex vivo confocal laser scanning microscopy

Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin

Improving utility of brain tumor confocal laser endomicroscopy: objective value assessment and diagnostic frame detection with convolutional neural networks

Confocal laser endomicroscopy (CLE), although capable of obtaining images at cellular resolution during surgery of brain tumors in real time, creates as many non-diagnostic as diagnostic images. Non-useful images are often distorted due to relative motion between probe and brain or blood artifacts. Many images, however, simply lack diagnostic features immediately informative to the physician. Examining all the hundreds or thousands of images from a single case to discriminate diagnostic images from nondiagnostic ones can be tedious. Providing a real-time diagnostic value assessment of images (fast enough to be used during the surgical acquisition process and accurate enough for the pathologist to rely on) to automatically detect diagnostic frames would streamline the analysis of images and filter useful images for the pathologist/surgeon. We sought to automatically classify images as diagnostic or non-diagnostic. AlexNet, a deep-learning architecture, was used in a 4-fold cross validation manner. Our dataset includes 16,795 images (8572 nondiagnostic and 8223 diagnostic) from 74 CLE-aided brain tumor surgery patients. The ground truth for all the images is provided by the pathologist. Average model accuracy on test data was 91% overall (90.79 % accuracy, 90.94 % sensitivity and 90.87 % specificity). To evaluate the model reliability we also performed receiver operating characteristic (ROC) analysis yielding 0.958 average for the area under ROC curve (AUC). These results demonstrate that a deeply trained AlexNet network can achieve a model that reliably and quickly recognizes diagnostic CLE images.Comment: SPIE Medical Imaging: Computer-Aided Diagnosis 201

Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining

Histological staining of tissue biopsies, especially hematoxylin and eosin (H&E) staining, serves as the benchmark for disease diagnosis and comprehensive clinical assessment of tissue. However, the process is laborious and time-consuming, often limiting its usage in crucial applications such as surgical margin assessment. To address these challenges, we combine an emerging 3D quantitative phase imaging technology, termed quantitative oblique back illumination microscopy (qOBM), with an unsupervised generative adversarial network pipeline to map qOBM phase images of unaltered thick tissues (i.e., label- and slide-free) to virtually stained H&E-like (vH&E) images. We demonstrate that the approach achieves high-fidelity conversions to H&E with subcellular detail using fresh tissue specimens from mouse liver, rat gliosarcoma, and human gliomas. We also show that the framework directly enables additional capabilities such as H&E-like contrast for volumetric imaging. The quality and fidelity of the vH&E images are validated using both a neural network classifier trained on real H&E images and tested on virtual H&E images, and a user study with neuropathologists. Given its simple and low-cost embodiment and ability to provide real-time feedback in vivo, this deep learning-enabled qOBM approach could enable new workflows for histopathology with the potential to significantly save time, labor, and costs in cancer screening, detection, treatment guidance, and more.Comment: 30 pages, 9 main figures, 1 table, 5 supplementary figure

Virtual histological staining of unlabeled autopsy tissue

Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.Comment: 24 Pages, 7 Figure

Time-lapse study of neural networks using phase imaging with computational specificity (PICS)

In life sciences, fluorescent labeling techniques are used to study molecular structures and interactions of cells. However, this type of cell imaging has its own limitations, one of which is that the process of staining the cells could be toxic to the cells and possibly damage them. We are specifically interested in time-lapse imaging of live neurons to study their growth and proliferation. Neurodegenerative diseases are characterized by phenotypic differences in neuron growth and arborization. This thesis proposes a label-free digital staining method using the deep convolutional neural network to address the issues with the previous cell imaging method. Our results show that a deep neural network, when trained on phase images with correct fluorescent labels, can correctly learn the necessary morphological information to successfully predict MAP2 and Tau labels. This inference, in turn, allows us to classify axons from dendrites in live, unlabeled neurons.Ope

Glucocorticoid receptors modulate dendritic spine plasticity and microglia activity in an animal model of Alzheimer's disease

Abstract Chronic exposure to high circulating levels of glucocorticoids (GCs) may be a key risk factor for Alzheimer's Disease (AD) development and progression. In addition, hyper-activation of glucocorticoid receptors (GRs) induces brain alterations comparable to those produced by AD. In transgenic mouse models of AD, GCs increase the production of the most important and typical hallmarks of this dementia such as: Aβ40, Aβ42 and tau protein (both the total tau and its hyperphosphorylated isoforms). Moreover, GCs in brain are pivotal regulators of dendritic spine turnover and microglia activity, two phenomena strongly altered in AD. Although it is well-established that GCs primes the neuroinflammatory response in the brain to some stimuli, it is unknown whether or how GRs modulates dendritic spine plasticity and microglia activity in AD. In this study, we evaluated, using combined Golgi Cox and immunofluorescence techniques, the role of GR agonists and antagonists on dendritic spine plasticity and microglia activation in hippocampus of 3xTg-AD mice. We found that dexamethasone, an agonist of GRs, was able to significantly reduce dendritic spine density and induced proliferation and activation of microglia in CA1 region of hippocampus of 3xTg-AD mice at 6 and 10 months of age. On the contrary, the treatment with mifepristone, an antagonist of GRs, strongly enhanced dendritic spine density, decreased microglia density and improved the behavioural performance of 3xTg-AD mice. Additionally, primary microglial cells in vitro were directly activated by dexamethasone. Together, these data demonstrate that stress exacerbates AD and promotes a rapid progression of the pathology acting on both neurons and glial cells, supporting an important pro-inflammatory role of GC within CNS in AD. Consequently, these results further strengthen the need to test clinical interventions that correct GCs dysregulation as promising therapeutic strategy to delay the onset and slow down the progression of AD