Computer-aided extraction of select MRI markers of cerebral small vessel disease: A systematic review.

Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods for the examination of three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy classical image processing, classical machine learning, and deep learning studies were identified. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in the training data. While good performance metrics were achieved in local datasets, there have not been generalisable pipelines validated in different research and/or clinical cohorts. Future studies could consider pooling data from multiple sources to increase data size and diversity, and evaluating performance using both image processing metrics and associations with clinical measures.

Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning.

Cervical cancer is one of the most frequent cancers in women worldwide, yet the early detection and treatment of lesions via regular cervical screening have led to a drastic reduction in the mortality rate. However, the routine examination of screening as a regular health checkup of women is characterized as time-consuming and labor-intensive, while there is lack of characteristic phenotypic profile and quantitative analysis. In this research, over the analysis of a privately collected and manually annotated dataset of 130 cytological whole-slide images, the authors proposed a deep-learning diagnostic system to localize, grade, and quantify squamous cell abnormalities. The system can distinguish abnormalities at the morphology level, namely atypical squamous cells of undetermined significance, low-grade squamous intraepithelial lesion, high-grade squamous intraepithelial lesion, and squamous cell carcinoma, as well as differential phenotypes of normal cells. The case study covered 51 positive and 79 negative digital gynecologic cytology slides collected from 2016 to 2018. Our automatic diagnostic system demonstrated its sensitivity of 100% at slide-level abnormality prediction, with the confirmation with three pathologists who performed slide-level diagnosis and training sample annotations. In the cellular-level classification, we yielded an accuracy of 94.5% in the binary classification between normality and abnormality, and the AUC was above 85% for each subtype of epithelial abnormality. Although the final confirmation from pathologists is often a must, empirically, computer-aided methods are capable of the effective extraction, interpretation, and quantification of morphological features, while also making it more objective and reproducible.

Deep Learning for Object Detection, Classification and Tracking in Industry Applications.

Object detection, classification and tracking are three important computer vision techniques [...].

A robust method for high-precision quantification of the complex three-dimensional vasculatures acquired by X-ray microtomography.

The quantification of micro-vasculatures is important for the analysis of angiogenesis on which the detection of tumor growth or hepatic fibrosis depends. Synchrotron-based X-ray computed micro-tomography (SR-µCT) allows rapid acquisition of micro-vasculature images at micrometer-scale spatial resolution. Through skeletonization, the statistical features of the micro-vasculature can be extracted from the skeleton of the micro-vasculatures. Thinning is a widely used algorithm to produce the vascular skeleton in medical research. Existing three-dimensional thinning methods normally emphasize the preservation of topological structure rather than geometrical features in generating the skeleton of a volumetric object. This results in three problems and limits the accuracy of the quantitative results related to the geometrical structure of the vasculature. The problems include the excessively shortened length of elongated objects, eliminated branches of blood vessel tree structure, and numerous noisy spurious branches. The inaccuracy of the skeleton directly introduces errors in the quantitative analysis, especially on the parameters concerning the vascular length and the counts of vessel segments and branching points. In this paper, a robust method using a consolidated end-point constraint for thinning, which generates geometry-preserving skeletons in addition to maintaining the topology of the vasculature, is presented. The improved skeleton can be used to produce more accurate quantitative results. Experimental results from high-resolution SR-µCT images show that the end-point constraint produced by the proposed method can significantly improve the accuracy of the skeleton obtained using the existing ITK three-dimensional thinning filter. The produced skeleton has laid the groundwork for accurate quantification of the angiogenesis. This is critical for the early detection of tumors and assessing anti-angiogenesis treatments.

The ubiquitin ligase RNF43 downregulation increases membrane expression of frizzled receptor in pancreatic ductal adenocarcinoma.

RNF43 is a novel tumor suppressor protein and known to be expressed in a multitude of tissue and dysregulated in cancers of these organs including ovarian and colorectal tissues. RNF43 expression has been shown to be expressed in mutated forms in several pancreatic cell lines. RNF43, by virtue of being an ubiquitin ligase, has the potential to ubiquitinylate membrane receptors like frizzled that subserves sensing Wnt soluble signals at the cell membrane. Thus, normally, RNF43 downregulates Wnt signaling by removing frizzled receptor from the membrane. In the present study, the expression of the tumor suppressor RNF43 was examined in human patient samples of pancreatic ductal adenocarcinoma (PDAC). Reduced levels of expression of RNF43 in PDAC were demonstrated by Western blotting. We incorporated membrane biotinylation assay to examine the expression of frizzled6 receptor in the membrane and demonstrated that it is significantly increased in PDAC tissues. This may be responsible for enhanced Wnt/beta-catenin signaling and provides the first level of evidence of a possible role of this well-known pathway in pancreatic exocrine carcinogenesis. We have utilized appropriate controls to ensure the true positivity of the findings of the present study. The contribution of Wnt/beta-catenin/RNF43 pathway in pancreatic carcinogenesis may provide for utilization of pharmacologic resources for precision-based approaches to treat pancreatic ductal adenocarcinoma.

Towards automated quantitative vasculature understanding via ultra high-resolution imagery.

This chapter presents an approach to processing ultra high-resolution, large-size biomedical imaging data for the purposes of detecting and quantifying vasculature and microvasculature . Capturing early signs of any changes in vasculature may have significant values for early-diagnosis and treatment assessment due to the well understood observation that vascular changes precede cancerous growth and metastasis metastasis . With the advent of key enabling technologies for extremely high-resolution imaging, such as synchrotron radiation synchrotron radiation based computed tomography (CT) computed tomography , the required levels of detail have become accessible. However, these technologies also present challenges in data analysis. This chapter aims to offer some insights as to how these changes might be best dealt with. We argue that the necessary steps in quantitative understanding of vasculatures include targeted data enhancement enhancement , information reduction aimed at characterizing the linear structure of vessels vessels , and quantitatively describing the vessel hierarchy. We present results on cerebral and liver vasculatures of a mouse captured at the Shanghai Synchrotron Radiation Facility (SSRF). These results were achieved with a processing pipeline comprising of our empirically selected component for each of the above steps. Towards the end, we discuss how alternative and additional components may be incorporated for improved speed and robustness.

Cloud based toolbox for image analysis, processing and reconstruction tasks.

This chapter describes a novel way of carrying out image analysis, reconstruction and processing tasks using cloud based service provided on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) infrastructure. The toolbox allows users free access to a wide range of useful blocks of functionalities (imaging functions) that can be connected together in workflows allowing creation of even more complex algorithms that can be re-run on different data sets, shared with others or additionally adjusted. The functions given are in the area of cellular imaging, advanced X-ray image analysis, computed tomography and 3D medical imaging and visualisation. The service is currently available on the website www.cloudimaging.net.au .

Identification of MicroRNAs and target genes involvement in hepatocellular carcinoma with microarray data.

The aim of the study is to identify the differentially expressed microRNAs (miRNAs) between hepatocellular carcinoma (HCC) samples and controls and provide new diagnostic potential miRNAs for HCC. The miRNAs expression profile data GSE20077 included 7 HCC samples, 1 HeLa sample and 3 controls. Differentially expressed miRNAs (DE-miRNAs) were identified by t-test and wilcox test. The miRNA with significantly differential expression was chosen for further analysis. Target genes for this miRNA were selected using TargetScan and miRbase database. STRING software was applied to construct the target genes interaction network and topology analysis was carried out to identify the hub gene in the network. And we identified the mechanism for affecting miRNA function. A total of 54 differentially expressed miRNAs were identified, in which there were 13 miRNAs published to be related to HCC. The differentially expressed hsa-miR-106b was chosen for further analysis and PTPRT (Receptor-type tyrosine-protein phosphatase T) was its potential target gene. The target genes interaction network was constructed among 33 genes, in which PTPRT was the hub gene. We got the conclusion that the differentially expressed hsa-miR-106b may play an important role in the development of HCC by regulating the expression of its potential target gene PT-PRT.

Comparison analysis in synchronous and metachronous metastatic colorectal cancer based on microarray expression profile.

BACKGROUND/AIMS: Colorectal cancer (CRC) is one of the most common malignancies, and liver metastasis is one of the major causes of death of CRC. This study aimed to compare the genetic difference between metachronous lesions (MC) and synchronous lesions (SC) and explore the molecular pathology of CRC metastasis. METHODOLOGY: Microarray expression profile data (GSE10961) including 8 MC and 10 SC was downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between the two groups were identified based on T test. Furthermore, GO enrichment analysis was performed for the down-regulated DEGs using DAVID. Finally, Classify validation of known CRC genes based on previous studies between MC and SC samples was conducted. RESULTS: Total of 36 DEGs including 35 down-regulated DEGs and 1 up-regulated DEGs were identified. The expressional differences of the 5 informative oncogenes: EGFr, PIK3R1, PTGS2 (COX-2), PTGS1 (COX1), and ALOX5AP between SC and MC were really tiny. CONCLUSIONS: Some DEGs, such as NFAT5, OLR1, ERAP2, HOXC6 and TWIST1 might play crucial roles in the regulation of CRC metastasis (both SC and MC) and by disrupting some pathways. However, our results indeed demand further research and experiment.

3 directional Inception-ResUNet: Deep spatial feature learning for multichannel singing voice separation with distortion.

Singing voice separation on robots faces the problem of interpreting ambiguous auditory signals. The acoustic signal, which the humanoid robot perceives through its onboard microphones, is a mixture of singing voice, music, and noise, with distortion, attenuation, and reverberation. In this paper, we used the 3D Inception-ResUNet structure in the U-shaped encoding and decoding network to improve the utilization of the spatial and spectral information of the spectrogram. Multiobjectives were used to train the model: magnitude consistency loss, phase consistency loss, and magnitude correlation consistency loss. We recorded the singing voice and accompaniment derived from the MIR-1K dataset with NAO robots and synthesized the 10-channel dataset for training the model. The experimental results show that the proposed model trained by multiple objectives reaches an average NSDR of 11.55 dB on the test dataset, which outperforms the comparison model.

DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images.

Accurate and early detection of pneumoconiosis using chest X-rays (CXR) is important for preventing the progression of this incurable disease. It is also a challenging task due to large variations in appearance, size and location of lesions in the lung regions as well as inter-class similarity and intra-class variance. Compared to traditional methods, Convolutional Neural Networks-based methods have shown improved results; however, these methods are still not applicable in clinical practice due to limited performance. In some cases, limited computing resources make it impractical to develop a model using whole CXR images. To address this problem, the lung fields are divided into six zones, each zone is classified separately and the zone classification results are then aggregated into an image classification score, based on state-of-the-art. In this study, we propose a dual lesion attention network (DLA-Net) for the classification of pneumoconiosis that can extract features from affected regions in a lung. This network consists of two main components: feature extraction and feature refinement. Feature extraction uses the pre-trained Xception model as the backbone to extract semantic information. To emphasise the lesion regions and improve the feature representation capability, the feature refinement component uses a DLA module that consists of two sub modules: channel attention (CA) and spatial attention (SA). The CA module focuses on the most important channels in the feature maps extracted by the backbone model, and the SA module highlights the spatial details of the affected regions. Thus, both attention modules combine to extract discriminative and rich contextual features to improve classification performance on pneumoconiosis. Experimental results show that the proposed DLA-Net outperforms state-of-the-art methods for pneumoconiosis classification.

AMFP-net: Adaptive multi-scale feature pyramid network for diagnosis of pneumoconiosis from chest X-ray images.

Early detection of pneumoconiosis by routine health screening of workers in the mining industry is critical for preventing the progression of this incurable disease. Automated pneumoconiosis classification in chest X-ray images is challenging due to the low contrast of opacities, inter-class similarity, intra-class variation and the existence of artifacts. Compared to traditional methods, convolutional neural networks have shown significant improvement in pneumoconiosis classification tasks, however, accurate classification remains challenging due to mainly the inability to focus on semantically meaningful lesion opacities. Most existing networks focus on high level abstract information and ignore low level detailed object information. Different from natural images where an object occupies large space, the classification of pneumoconiosis depends on the density of small opacities inside the lung. To address this issue, we propose a novel two-stage adaptive multi-scale feature pyramid network called AMFP-Net for the diagnosis of pneumoconiosis from chest X-rays. The proposed model consists of 1) an adaptive multi-scale context block to extract rich contextual and discriminative information and 2) a weighted feature fusion module to effectively combine low level detailed and high level global semantic information. This two-stage network first segments the lungs to focus more on relevant regions by excluding irrelevant parts of the image, and then utilises the segmented lungs to classify pneumoconiosis into different categories. Extensive experiments on public and private datasets demonstrate that the proposed approach can outperform state-of-the-art methods for both segmentation and classification.

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: The COSMIC consortium.

INTRODUCTION: White matter hyperintensities (WMHs) are an important imaging marker for cerebral small vessel diseases, but their risk factors and cognitive associations have not been well documented in populations of different ethnicities and/or from different geographical regions. METHODS: We investigated how WMHs were associated with vascular risk factors and cognition in both Whites and Asians, using data from five population-based cohorts of non-demented older individuals from Australia, Singapore, South Korea, and Sweden (N = 1946). WMH volumes (whole brain, periventricular, and deep) were quantified with UBO Detector and harmonized using the ComBat model. We also harmonized various vascular risk factors and scores for global cognition and individual cognitive domains. RESULTS: Factors associated with larger whole brain WMH volumes included diabetes, hypertension, stroke, current smoking, body mass index, higher alcohol intake, and insufficient physical activity. Hypertension and stroke had stronger associations with WMH volumes in Whites than in Asians. No associations between WMH volumes and cognitive performance were found after correction for multiple testing. CONCLUSION: The current study highlights ethnic differences in the contributions of vascular risk factors to WMHs.

A Multi-Scale Context Aware Attention Model for Medical Image Segmentation.

Medical image segmentation is critical for efficient diagnosis of diseases and treatment planning. In recent years, convolutional neural networks (CNN)-based methods, particularly U-Net and its variants, have achieved remarkable results on medical image segmentation tasks. However, they do not always work consistently on images with complex structures and large variations in regions of interest (ROI). This could be due to the fixed geometric structure of the receptive fields used for feature extraction and repetitive down-sampling operations that lead to information loss. To overcome these problems, the standard U-Net architecture is modified in this work by replacing the convolution block with a dilated convolution block to extract multi-scale context features with varying sizes of receptive fields, and adding a dilated inception block between the encoder and decoder paths to alleviate the problem of information recession and the semantic gap between features. Furthermore, the input of each dilated convolution block is added to the output through a squeeze and excitation unit, which alleviates the vanishing gradient problem and improves overall feature representation by re-weighting the channel-wise feature responses. The original inception block is modified by reducing the size of the spatial filter and introducing dilated convolution to obtain a larger receptive field. The proposed network was validated on three challenging medical image segmentation tasks with varying size ROIs: lung segmentation on chest X-ray (CXR) images, skin lesion segmentation on dermoscopy images and nucleus segmentation on microscopy cell images. Improved performance compared to state-of-the-art techniques demonstrates the effectiveness and generalisability of the proposed Dilated Convolution and Inception blocks-based U-Net (DCI-UNet).

Ordered growth of metal oxides in patterned multi-angle microstructures.

Herein, we report a facile method combining top-down patterning transfer and bottom-up nanorod growth for preparing large-area and ordered TiO2 nanorod arrays. Pre-crystallization seeding was patterned with nanostructured morphologies via interfacial tension-driven precursor solution scattering on various types and period templates. This is a widely applicable strategy for capillary force-driven interfacial patterns, which also shows great operability in complex substrate morphologies with multiple-angle mixing. Moreover, the customized patterned lithographic templates containing English words, Arabic numerals, and Chinese characters are used to verify the applicability and controllability of this hybrid method. In general, our work provides a versatile strategy for the low-cost and facile preparation of hydrothermally growable metal oxide (e.g., ZnO and MnO2) nanostructures with potential applications in the fields of microelectronic devices, photoelectric devices, energy storage, and photocatalysis.

Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging.

BACKGROUND AND OBJECTIVES: Advanced artificial intelligence and machine learning have great potential to redefine how skin lesions are detected, mapped, tracked and documented. Here, we propose a 3D whole-body imaging system known as 3DSkin-mapper to enable automated detection, evaluation and mapping of skin lesions. METHODS: A modular camera rig arranged in a cylindrical configuration was designed to automatically capture images of the entire skin surface of a subject synchronously from multiple angles. Based on the images, we developed algorithms for 3D model reconstruction, data processing and skin lesion detection and tracking based on deep convolutional neural networks. We also introduced a customised, user-friendly, and adaptable interface that enables individuals to interactively visualise, manipulate, and annotate the images. The interface includes built-in features such as mapping 2D skin lesions onto the corresponding 3D model. RESULTS: The proposed system is developed for skin lesion screening, the focus of this paper is to introduce the system instead of clinical study. Using synthetic and real images we demonstrate the effectiveness of the proposed system by providing multiple views of a target skin lesion, enabling further 3D geometry analysis and longitudinal tracking. Skin lesions are identified as outliers which deserve more attention from a skin cancer physician. Our detector leverages expert annotated labels to learn representations of skin lesions, while capturing the effects of anatomical variability. It takes only a few seconds to capture the entire skin surface, and about half an hour to process and analyse the images. CONCLUSIONS: Our experiments show that the proposed system allows fast and easy whole body 3D imaging. It can be used by dermatological clinics to conduct skin screening, detect and track skin lesions over time, identify suspicious lesions, and document pigmented lesions. The system can potentially save clinicians time and effort significantly. The 3D imaging and analysis has the potential to change the paradigm of whole body photography with many applications in skin diseases, including inflammatory and pigmentary disorders. With reduced time requirements for recording and documenting high-quality skin information, doctors could spend more time providing better-quality treatment based on more detailed and accurate information.

Space-Confined seeding and growth of ordered arrays of TiO2 hierarchical nanostructures.

Here, we report a facile approach to fabricate large area ordered arrays of TiO2 hierarchical nanostructures through space-confined seeding and growth on inverted pyramid templates. The mechanisms of space-confined seeding and growth have been systematically explored and studied. The drying TiO2 seed precursor solution prefers to accumulate on the narrow structures including the centre and edges of the inverted pyramid structures, which facilitates to reduce the free energy of the precursor solution surface and form crystal seeds. Followed by hydrothermal treatment, selective growth of TiO2 hierarchical nanostructures on desirable locations, such as only on the centre, only on the edges, or on the entire surface of the inverted pyramid templates, can be achieved. In addition, the growth temperature, duration and solvents affect the morphology of TiO2 hierarchical nanostructures. This work may provide a universal approach to obtain ordered arrays of metal oxide (e.g. ZnO and MnO2, etc.) nanostructures for applications in optics, electrics, energy, and catalysis.

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: the COSMIC consortium.

INTRODUCTION: White matter hyperintensities (WMH) are an important imaging marker for cerebral small vessel diseases, but their risk factors and cognitive associations have not been well-documented in populations of different ethnicities and/or from different geographical regions. METHOD: Magnetic resonance imaging data of five population-based cohorts of non-demented older individuals from Australia, Singapore, South Korea, and Sweden (N = 1,946) were examined for WMH and their associations with vascular risk factors and cognition. RESULT: Factors associated with larger whole brain WMH volumes included diabetes, hypertension, stroke, current smoking, body mass index, higher alcohol intake and insufficient physical activity. Participants with moderate or higher physical activity had less WMH than those who never exercised, but the former two groups did not differ. Hypertension and stroke had stronger associations with WMH volumes in the White, compared to Asian subsample. DISCUSSION: The current study highlighted the ethnic differences in the contributions of vascular risk factors to WMH.

Weakly Supervised RGB-D Salient Object Detection With Prediction Consistency Training and Active Scribble Boosting.

RGB-D salient object detection (SOD) has attracted increasingly more attention as it shows more robust results in complex scenes compared with RGB SOD. However, state-of-the-art RGB-D SOD approaches heavily rely on a large amount of pixel-wise annotated data for training. Such densely labeled annotations are often labor-intensive and costly. To reduce the annotation burden, we investigate RGB-D SOD from a weakly supervised perspective. More specifically, we use annotator-friendly scribble annotations as supervision signals for model training. Since scribble annotations are much sparser compared to ground-truth masks, some critical object structure information might be neglected. To preserve such structure information, we explicitly exploit the complementary edge information from two modalities (i.e., RGB and depth). Specifically, we leverage the dual-modal edge guidance and introduce a new network architecture with a dual-edge detection module and a modality-aware feature fusion module. In order to use the useful information of unlabeled pixels, we introduce a prediction consistency training scheme by comparing the predictions of two networks optimized by different strategies. Moreover, we develop an active scribble boosting strategy to provide extra supervision signals with negligible annotation cost, leading to significant SOD performance improvement. Extensive experiments on seven benchmarks validate the superiority of our proposed method. Remarkably, the proposed method with scribble annotations achieves competitive performance in comparison to fully supervised state-of-the-art methods.

Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays.

Pneumoconiosis is a group of occupational lung diseases induced by mineral dust inhalation and subsequent lung tissue reactions. It can eventually cause irreparable lung damage, as well as gradual and permanent physical impairments. It has affected millions of workers in hazardous industries throughout the world, and it is a leading cause of occupational death. It is difficult to diagnose early pneumoconiosis because of the low sensitivity of chest radiographs, the wide variation in interpretation between and among readers, and the scarcity of B-readers, which all add to the difficulty in diagnosing these occupational illnesses. In recent years, deep machine learning algorithms have been extremely successful at classifying and localising abnormality of medical images. In this study, we proposed an ensemble learning approach to improve pneumoconiosis detection in chest X-rays (CXRs) using nine machine learning classifiers and multi-dimensional deep features extracted using CheXNet-121 architecture. There were eight evaluation metrics utilised for each high-level feature set of the associated cross-validation datasets in order to compare the ensemble performance and state-of-the-art techniques from the literature that used the same cross-validation datasets. It is observed that integrated ensemble learning exhibits promising results (92.68% accuracy, 85.66% Matthews correlation coefficient (MCC), and 0.9302 area under the precision-recall (PR) curve), compared to individual CheXNet-121 and other state-of-the-art techniques. Finally, Grad-CAM was used to visualise the learned behaviour of individual dense blocks within CheXNet-121 and their ensembles into three-color channels of CXRs. We compared the Grad-CAM-indicated ROI to the ground-truth ROI using the intersection of the union (IOU) and average-precision (AP) values for each classifier and their ensemble. Through the visualisation of the Grad-CAM within the blue channel, the average IOU passed more than 90% of the pneumoconiosis detection in chest radiographs.