Development of an automated combined positive score prediction pipeline using artificial intelligence on multiplexed immunofluorescence images.

Immunotherapy targeting immune checkpoint proteins, such as programmed cell death ligand 1 (PD-L1), has shown impressive outcomes in many clinical trials but only 20%-40% of patients benefit from it. Utilizing Combined Positive Score (CPS) to evaluate PD-L1 expression in tumour biopsies to identify patients with the highest likelihood of responsiveness to anti-PD-1/PD-L1 therapy has been approved by the Food and Drug Administration for several solid tumour types. Current CPS workflow requires a pathologist to manually score the two-colour PD-L1 chromogenic immunohistochemistry image.

Blur-Robust Nuclei Segmentation for Immunofluorescence Images.

Automated nuclei segmentation from immunofluorescence (IF) microscopic image is a crucial first step in digital pathology. A lot of research has been devoted to develop novel nuclei segmentation algorithms to give high performance on good quality images. However, fewer methods were developed for poor-quality images like out-of-focus (blurry) data.

Dual Encoder Attention U-net for Nuclei Segmentation.

Nuclei segmentation in whole slide images (WSIs) stained with Hematoxylin and Eosin (H&E) dye, is a key step in computational pathology which aims to automate the laborious process of manual counting and segmentation. Nuclei segmentation is a challenging problem that involves challenges such as touching nuclei resolution, small-sized nuclei, size, and shape variations. With the advent of deep learning, convolution neural networks (CNNs) have shown a powerful ability to extract effective representations from microscopic H&E images.

Spatial-context-aware RNA-sequence prediction from head and neck cancer histopathology images.

Molecular profiling of the tumor in addition to the histological tumor analysis can provide robust information for targeted cancer therapies. Often such data are not available for analysis due to processing delays, cost or inaccessibility. In this paper, we proposed a deep learning-based method to predict RNA-sequence expression (RNA-seq) from Hematoxylin and Eosin whole-slide images (H&E WSI) in head and neck cancer patients.

Functional connectivity with cortical depth assessed by resting state fMRI of subregions of S1 in squirrel monkeys.

Whereas resting state blood oxygenation-level dependent (BOLD) functional MRI has been widely used to assess functional connectivity between cortical regions, the laminar specificity of such measures is poorly understood. This study aims to determine: (a) whether the resting state functional connectivity (rsFC) between two functionally related cortical regions varies with cortical depth, (b) the relationship between layer-resolved tactile stimulus-evoked activation pattern and interlayer rsFC pattern between two functionally distinct but related somatosensory areas 3b and 1, and (c) the effects of spatial resolution on rsFC measures. We examined the interlayer rsFC between areas 3b and 1 of squirrel monkeys under anesthesia using tactile stimulus-driven and resting state BOLD acquisitions at submillimeter resolution.

Alzheimer's disease and amnestic mild cognitive impairment weaken connections within the default-mode network: a multi-modal imaging study.

We applied a multi-modal imaging approach to examine structural and functional alterations in the default-mode network (DMN) that are associated with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI), a transitional phase between healthy cognitive aging and dementia. Subjects included 10 patients with probable AD, 11 patients with aMCI, and 12 age- and education-matched normal controls (NC). Whole-brain resting-state functional, diffusion-weighted, and volumetric magnetic resonance imaging (MRI) data as well as 18F-fluorodeoxyglucose-based positron emission tomography (FDG-PET) data were acquired.

Integration of resting-state FMRI and diffusion-weighted MRI connectivity analyses of the human brain: limitations and improvement.

Background: Integration of functional connectivity analysis based on resting-state functional Magnetic Resonance Imaging (fMRI) and structural connectivity analysis based on Diffusion-Weighted Imaging (DWI) has shown great potential to improve understanding of the neural networks in the human brain. However, there are sensitivity and specificity-related interpretation issues that must be addressed.

Methods: We assessed the long-range functional and structural connections of the default-mode, attention, visual and motor networks on 25 healthy subjects.

A diffusion gradient optimization framework for spinal cord diffusion tensor imaging.

The uncertainty in the estimation of diffusion model parameters in diffusion tensor imaging (DTI) can be reduced by optimally selecting the diffusion gradient directions utilizing some prior structural information. This is beneficial for spinal cord DTI, where the magnetic resonance images have low signal-to-noise ratio and thus high uncertainty in diffusion model parameter estimation. Presented is a gradient optimization scheme based on D-optimality, which reduces the overall estimation uncertainty by minimizing the Rician Cramer-Rao lower bound of the variance of the model parameter estimates.