Deep learning based registration of serial whole-slide histopathology images in different stains.

For routine pathology diagnosis and imaging-based biomedical research, Whole-slide image (WSI) analyses have been largely limited to a 2D tissue image space. For a more definitive tissue representation to support fine-resolution spatial and integrative analyses, it is critical to extend such tissue-based investigations to a 3D tissue space with spatially aligned serial tissue WSIs in different stains, such as Hematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) biomarkers. However, such WSI registration is technically challenged by the overwhelming image scale, the complex histology structure change, and the significant difference in tissue appearances in different stains. The goal of this study is to register serial sections from multi-stain histopathology whole-slide image blocks. We propose a novel translation-based deep learning registration network CGNReg that spatially aligns serial WSIs stained in H&E and by IHC biomarkers without prior deformation information for the model training. First, synthetic IHC images are produced from H&E slides through a robust image synthesis algorithm. Next, the synthetic and the real IHC images are registered through a Fully Convolutional Network with multi-scaled deformable vector fields and a joint loss optimization. We perform the registration at the full image resolution, retaining the tissue details in the results. Evaluated with a dataset of 76 breast cancer patients with 1 H&E and 2 IHC serial WSIs for each patient, CGNReg presents promising performance as compared with multiple state-of-the-art systems in our evaluation. Our results suggest that CGNReg can produce promising registration results with serial WSIs in different stains, enabling integrative 3D tissue-based biomedical investigations.

Model-free prediction of multistability using echo state network.

In the field of complex dynamics, multistable attractors have been gaining significant attention due to their unpredictability in occurrence and extreme sensitivity to initial conditions. Co-existing attractors are abundant in diverse systems ranging from climate to finance and ecological to social systems. In this article, we investigate a data-driven approach to infer different dynamics of a multistable system using an echo state network. We start with a parameter-aware reservoir and predict diverse dynamics for different parameter values. Interestingly, a machine is able to reproduce the dynamics almost perfectly even at distant parameters, which lie considerably far from the parameter values related to the training dynamics. In continuation, we can predict whole bifurcation diagram significant accuracy as well. We extend this study for exploring various dynamics of multistable attractors at an unknown parameter value. While we train the machine with the dynamics of only one attractor at parameter p, it can capture the dynamics of a co-existing attractor at a new parameter value p + Δ p. Continuing the simulation for a multiple set of initial conditions, we can identify the basins for different attractors. We generalize the results by applying the scheme on two distinct multistable systems.

Role of assortativity in predicting burst synchronization using echo state network.

In this study, we use a reservoir computing based echo state network (ESN) to predict the collective burst synchronization of neurons. Specifically, we investigate the ability of ESN in predicting the burst synchronization of an ensemble of Rulkov neurons placed on a scale-free network. We have shown that a limited number of nodal dynamics used as input in the machine can capture the real trend of burst synchronization in this network. Further, we investigate the proper selection of nodal inputs of degree-degree (positive and negative) correlated networks. We show that for a disassortative network, selection of different input nodes based on degree has no significant role in the machine's prediction. However, in the case of assortative network, training the machine with the information (i.e., time series) of low degree nodes gives better results in predicting the burst synchronization. The results are found to be consistent with the investigation carried out with a continuous time Hindmarsh-Rose neuron model. Furthermore, the role of hyperparameters like spectral radius and leaking parameter of ESN on the prediction process has been examined. Finally, we explain the underlying mechanism responsible for observing these differences in the prediction in a degree correlated network.

Assortativity-induced explosive synchronization in a complex neuronal network.

In this study, we consider a scale-free network of nonidentical Chialvo neurons, coupled through electrical synapses. For sufficiently strong coupling, the system undergoes a transition from completely out of phase synchronized to phase synchronized state. The principal focus of this study is to investigate the effect of the degree of assortativity over the synchronization transition process. It is observed that, depending on assortativity, bistability between two asymptotically stable states allows one to develop a hysteresis loop which transforms the phase transition from second order to first order. An expansion in the area of hysteresis loop is noticeable with increasing degree-degree correlation in the network. Our study also reveals that effective frequencies of nodes simultaneously go through a continuous or sudden transition to the synchronized state with the corresponding phases. Further, we examine the robustness of the results under the effect of network size and average degree, as well as diverse frequency setup. Finally, we investigate the dynamical mechanism in the process of generating explosive synchronization. We observe a significant impact of lower degree nodes behind such phenomena: in a positive assortative network the low degree nodes delay the synchronization transition.

Convolutional autoencoder based model HistoCAE for segmentation of viable tumor regions in liver whole-slide images.

Liver cancer is one of the leading causes of cancer deaths in Asia and Africa. It is caused by the Hepatocellular carcinoma (HCC) in almost 90% of all cases. HCC is a malignant tumor and the most common histological type of the primary liver cancers. The detection and evaluation of viable tumor regions in HCC present an important clinical significance since it is a key step to assess response of chemoradiotherapy and tumor cell proportion in genetic tests. Recent advances in computer vision, digital pathology and microscopy imaging enable automatic histopathology image analysis for cancer diagnosis. In this paper, we present a multi-resolution deep learning model HistoCAE for viable tumor segmentation in whole-slide liver histopathology images. We propose convolutional autoencoder (CAE) based framework with a customized reconstruction loss function for image reconstruction, followed by a classification module to classify each image patch as tumor versus non-tumor. The resulting patch-based prediction results are spatially combined to generate the final segmentation result for each WSI. Additionally, the spatially organized encoded feature map derived from small image patches is used to compress the gigapixel whole-slide images. Our proposed model presents superior performance to other benchmark models with extensive experiments, suggesting its efficacy for viable tumor area segmentation with liver whole-slide images.

Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.

Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients with fatty liver diseases and in the decision to use donor livers for transplantation. However, pathology reviewing processes, when conducted manually, are subject to a high inter- and intra-reader variability, due to the overwhelmingly large number and significantly varying appearance of steatosis instances. This process is challenging as there is a large number of overlapped steatosis droplets with either missing or weak boundaries. In this study, we propose a deep-learning-based region-boundary integrated network for precise steatosis quantification with whole slide liver histopathology images. The proposed model consists of two sequential steps: a region extraction and a boundary prediction module for foreground regions and steatosis boundary prediction, followed by an integrated prediction map generation. Missing steatosis boundaries are next recovered from the predicted map and assembled from adjacent image patches to generate results for the whole slide histopathology image. The resulting steatosis measures both at the pixel level and steatosis object-level present strong correlation with pathologist annotations, radiology readouts and clinical data. In addition, the segregated steatosis object count is shown as a promising alternative measure to the traditional metrics at the pixel level. These results suggest a high potential of artificial intelligence-assisted technology to enhance liver disease decision support using whole slide images.

Effective nuclei segmentation with sparse shape prior and dynamic occlusion constraint for glioblastoma pathology images.

We propose a segmentation method for nuclei in glioblastoma histopathologic images based on a sparse shape prior guided variational level set framework. By spectral clustering and sparse coding, a set of shape priors is exploited to accommodate complicated shape variations. We automate the object contour initialization by a seed detection algorithm and deform contours by minimizing an energy functional that incorporates a shape term in a sparse shape prior representation, an adaptive contour occlusion penalty term, and a boundary term encouraging contours to converge to strong edges. As a result, our approach is able to deal with mutual occlusions and detect contours of multiple intersected nuclei simultaneously. Our method is applied to several whole-slide histopathologic image datasets for nuclei segmentation. The proposed method is compared with other state-of-the-art methods and demonstrates good accuracy for nuclei detection and segmentation, suggesting its promise to support biomedical image-based investigations.

Analysis of Cellular Feature Differences of Astrocytomas with Distinct Mutational Profiles Using Digitized Histopathology Images.

Cellular phenotypic features derived from histopathology images are the basis of pathologic diagnosis and are thought to be related to underlying molecular profiles. Due to overwhelming cell numbers and population heterogeneity, it remains challenging to quantitatively compute and compare features of cells with distinct molecular signatures. In this study, we propose a self-reliant and efficient analysis framework that supports quantitative analysis of cellular phenotypic difference across distinct molecular groups. To demonstrate efficacy, we quantitatively analyze astrocytomas that are molecularly characterized as either Isocitrate Dehydrogenase (IDH) mutant (MUT) or wildtype (WT) using imaging data from The Cancer Genome Atlas database. Representative cell instances that are phenotypically different between these two groups are retrieved after segmentation, feature computation, data pruning, dimensionality reduction, and unsupervised clustering. Our analysis is generic and can be applied to a wide set of cell-based biomedical research.

Segmentation of Overlapped Steatosis in Whole-Slide Liver Histopathology Microscopy Images.

An accurate steatosis quantification with pathology tissue samples is of high clinical importance. However, such pathology measurement is manually made in most clinical practices, subject to severe reader variability due to large sampling bias and poor reproducibility. Although some computerized automated methods are developed to quantity the steatosis regions, they present limited analysis capacity for high resolution whole-slide microscopy images and accurate overlapped steatosis division. In this paper, we propose a method that extracts an individual whole tissue piece at high resolution with minimum background area by estimating tissue bounding box and rotation angle. This is followed by the segmentation and segregation of steatosis regions with high curvature point detection and an ellipse fitting quality assessment method. We validate our method with isolated and overlapped steatosis regions in liver tissue images of 11 patients. The experimental results suggest that our method is promising for enhanced support of steatosis quantization during the pathology review for liver disease treatment.

Assessment of Likelihood Ratio for Four Contact Dermatitis Symptoms of Vinca Minor.

BACKGROUND: Contact dermatitis (CD) is a frequently occurring medical condition, for which Vinca minor (VM) is one of the recommended homeopathic medicines. However, the symptoms indicating this medicine have not yet been assessed systematically. Likelihood ratio (LR), based on Bayesian statistics, may yield better estimation of a medicine's indication than the existing method of entry of symptoms into materia medica and repertories. METHODS: We investigated LRs of four CD symptoms of VM: (1) great sensitiveness of skin, with redness and soreness from slightest rubbing; (2) weeping eczema with foul, thick crusts; (3) itching amelioration in open air; and (4) CD of scalp. An observational, prospective, patient-outcome study was conducted in five different practice settings on 390 CD patients over 18 months using three outcomes-Glasgow Homeopathic Hospital Outcome Scale (GHHOS), Scoring Atopic Dermatitis (SCORAD), and Dermatology Life Quality Index (DLQI), assessed at baseline, after 3 and 6 months. The LR of each of the four symptoms was estimated as per the patient-rated outcomes on GHHOS. RESULTS: Seventy-four VM and 316 non-VM cases were analyzed. Estimated LRs were as follows: symptom 1, 1.29 (95% confidence interval [CI]: 0.65 to 2.60); symptom 2, 1.48 (95% CI: 0.80 to 2.74); symptom 3, 1.70 (95% CI: 0.94 to 3.07); symptom 4, 1.36 (95% CI: 0.74 to 2.51). There were statistically significant reductions in SCORAD and DLQI scores over 3 and 6 months. CONCLUSION: There was insufficient evidence to attribute any of the four assessed symptoms clearly to VM. Though non-significant, a high LR was observed for "itching amelioration in open air" (symptom 3). Symptoms in the homeopathic materia medica for VM are perhaps over-represented. More research of this nature is warranted.

Technology acceptance perception for promotion of sustainable consumption.

Economic growth in the past decades has resulted in change in consumption pattern and emergence of tech-savvy generation with unprecedented increase in the usage of social network technology. In this paper, the technology acceptance value gap adapted from the technology acceptance model has been applied as a tool supporting social network technology usage and subsequent promotion of sustainable consumption. The data generated through the use of structured questionnaires have been analyzed using structural equation modeling. The validity of the model and path estimates signifies the robustness of Technology Acceptance value gap in adjudicating the efficiency of social network technology usage in augmentation of sustainable consumption and awareness. The results indicate that subjective norm gap, ease-of-operation gap, and quality of green information gap have the most adversarial impact on social network technology usage. Eventually social networking technology usage has been identified as a significant antecedent of sustainable consumption.

Colorado Plateau magmatism and uplift by warming of heterogeneous lithosphere.

The forces that drove rock uplift of the low-relief, high-elevation, tectonically stable Colorado Plateau are the subject of long-standing debate. While the adjacent Basin and Range province and Rio Grande rift province underwent Cenozoic shortening followed by extension, the plateau experienced approximately 2 km of rock uplift without significant internal deformation. Here we propose that warming of the thicker, more iron-depleted Colorado Plateau lithosphere over 35-40 Myr following mid-Cenozoic removal of the Farallon plate from beneath North America is the primary mechanism driving rock uplift. In our model, conductive re-equilibration not only explains the rock uplift of the plateau, but also provides a robust geodynamic interpretation of observed contrasts between the Colorado Plateau margins and the plateau interior. In particular, the model matches the encroachment of Cenozoic magmatism from the margins towards the plateau interior at rates of 3-6 km Myr(-1) and is consistent with lower seismic velocities and more negative Bouguer gravity at the margins than in the plateau interior. We suggest that warming of heterogeneous lithosphere is a powerful mechanism for driving epeirogenic rock uplift of the Colorado Plateau and may be of general importance in plate-interior settings.