Standard Image Analysis Research Articles

New deep AI‐based algorithm to assess quality of primary corneal endothelial cell cultures

Aims/Purpose: The development of cell therapies to treat corneal endothelial pathologies requires characterizing the cells during the cell culture process. The endothelial cell density (ECD) and morphometry are essential parameters for identifying cultures with sufficient phenotype and yield. However, they are difficult to measure on cells in culture using conventional image analysis tools. Aims: (1) To develop an AI‐based automatic cell segmentation method for reliable ECD and morphology measurement. (2) To select new morphometry parameters. (3) To classify endothelial cell cultures by quality.Methods: Experiments were conducted on primary cultures of human corneal endothelial cells (hCECs) after immunofluorescent labeling against NCAM revealing lateral membranes. Cultures from donors < 30 years old (high ECD and regular mosaic) were compared with cultures form older donors comprising senescent cells and cells in endothelial‐mesenchymal transition. Images were acquired with an epifluorescence microscope. We compared two AI segmentation models, a custom model and the published Cellpose model1,2. Models were trained with manually labeled images and tested on separate images. Standard image analysis methods in Python measured cell number, morphology, comparing reference and AI‐generated images3. Additional metrics were developed to qualify endothelial cell culture and correlated with known metrics. K‐Means and other classifications were tested to classify cell culture quality.Results: Cellpose outperformed the custom model in segmentation accuracy (mean relative error ± SD respectively: 0.054 ± 0.020 vs 0.0499 ± 0.0136). New reliable morphological measurements were defined, enabling classification of endothelial cell culture clusters based on quality. Coronal CV (coefficient of variation) was developed to complement classical CV used to characterize clinical endothelial cell.Conclusions: AI segmentation of hCEC in culture significantly improves quality measurement during bioengineering, facilitating rapid analysis and classification of cell batches, thereby enhancing quality assessment and success rates for cell‐based endothelial therapy.References Stringer et al (2020). Pachitariu et al (2022). Gavet et al (2012).

Fetal and neonatal echocardiographic analysis of biomechanical alterations for the systemic right ventricle heart

Background The perinatal transition’s impact on systemic right ventricle (SRV) cardiac hemodynamics is not fully understood. Standard clinical image analysis tools fall short of capturing comprehensive diastolic and systolic measures of these hemodynamics. Objectives Compare standard and novel hemodynamic echocardiogram (echo) parameters to quantify perinatal changes in SRV and healthy controls. Methods We performed a retrospective study of 10 SRV patients with echocardiograms at 33-weeks gestation and at day of birth and 12 age-matched controls. We used in-house developed analysis algorithms to quantify ventricular biomechanics from four-chamber B-mode and color Doppler scans. Cardiac morphology, hemodynamics, tissue motion, deformation, and flow parameters were measured. Results Tissue motion, deformation, and index measurements did not reliably capture biomechanical changes. Stroke volume and cardiac output were nearly twice as large for the SRV compared to the control RV and left ventricle (LV) due to RV enlargement. The enlarged RV exhibited disordered flow with higher energy loss (EL) compared to prenatal control LV and postnatal control RV and LV. Furthermore, the enlarged RV demonstrated elevated vortex strength (VS) and kinetic energy (KE) compared to both the control RV and LV, prenatally and postnatally. The SRV showed reduced relaxation with increased early filling velocity (E) compared prenatally to the LV and postnatally to the control RV and LV. Furthermore, increased recovery pressure (ΔP) was observed between the SRV and control RV and LV, prenatally and postnatally. Conclusions The novel hydrodynamic parameters more reliably capture the SRV alterations than traditional parameters.

Enhancing chromosomal analysis efficiency through deep learning-based artificial intelligence graphic analysis

The objective of this study is to evaluate the efficacy and diagnostic utility of an advanced chromosomal analysis approach. A total of 2663 amniotic fluid samples were chosen for chromosomal karyotype profiling between January 2022 and June 2023. Two sets of tests were carried out: experiment 1 involved randomly selecting 1168 examples to test the accuracy of machine learning-based chromosomal karyotypes. The aim was to determine the method’s general applicability when cases were naturally dispersed. Experiment 2 concentrated on randomly selecting the most common examples of chromosomal number anomalies and cases with structural defects that did not affect the visual assessment of chromosome categories. The goal was to investigate the diagnostic efficacy of the artificial intelligence (AI) analysis system in detecting these flaws. The results of experiment 1 demonstrated the resilience of the intelligent analysis system in cases with significant differences in chromosomal karyotypes, resulting from manual shooting and film-making. Experiment 2 results showed that the intelligent analysis system surpassed the standard chromosomal image analysis program in terms of automated analysis accuracy, for both normal and defect cases. Furthermore, the intelligent analysis system demonstrated detection and analysis speeds that were 3–15 times faster. The average speed of regular case analysis increased by a factor of 4–6, cases with quantitative defects increased by a factor of 3–5, and cases with structural defects increased by a factor of 5–7. Implementing a chromosome intelligence analysis system in clinical practice could improve the efficiency of chromosome identification and analysis, allow for more widespread chromosomal examination, and reduce the likelihood of congenital defects.

Abstract 6176: Identifying poor prognosis stage I lung adenocarcinoma patients through novel morphological biomarker based on computational pathology

Abstract Current practice for evaluating prognosis of resectable lung adenocarcinoma (LUAD) patients relies on the Tumor, Node, Metastasis staging system. Despite low relapse risk, post-recurrence survival in a stage I population is poor with a median post relapse survival of around 25 months. There is a need for a biomarker that can enrich for stage I patients with relapse risk to identify a population likely to benefit from adjuvant therapy. In this study, we aim to identify a predictive biomarker of relapse in early-stage lung cancer following surgical resection using AI-based computational pathology. The retrospective patient cohort analyzed in this study consisted of a total of 166 patients that underwent surgical resection for a clinical stage I LUAD. Out of these, 54 patients experienced disease recurrence within 5 years, and 112 patients had a confirmed disease-free period of at least 5 years post-surgery. For each patient, at least one digitized slide (average = 2.46) stained with haematoxylin and eosin (H&E) was available for analysis. The project was accepted by the IUCPQ ethics committee (2022-3751, 22138). Manual annotations delineating the tumor core (TC) were drawn by pathologists. We then applied several proprietary image analysis models capable of distinguishing epithelium, stroma, and necrosis within the tumor tissue as well as detecting tumor infiltrating lymphocytes (TILs) and cell nuclei. In this project, we additionally introduce a novel approach rooted in mathematical graph theory to encoding topological aspects of the tissue morphology. A cell-graph constructed from the positions of cell nuclei was used to calculate cell-level graph-theoretic measures which were then aggregated to the slide-level by computing the median. In total, 23 data readouts were obtained of which 14 were based on the standard image analysis pipeline alone, and a further 9 originated from our newly developed graph-based approach. We identified promising biomarkers using the Wilcoxon rank-sum test and the area under the ROC curve (AUROC); robustness of this evaluation was investigating using resampling methods, using 100 bootstrap samples and 100 repeats of 3-fold cross-validation, respectively. This allowed us to evaluate the prognostic value of the biomarkers with respect to 5-year relapse or death. We were able to identify the best prognostic biomarker for relapse to be the median weighted clustering coefficient (CC) across all cells in the TC area, which is derived from the graph-based analyses. The median CC was able to achieve a whole-cohort AUROC of 0.695 (average cross-validated AUROC = 0.675, 95% interval 61.7%-70.4%) as well as a relapse vs relapse-free Wilcoxon test p-value of 4.65x10-5 (bootstrapped average = 0.0002). In addition, we were also able to demonstrate that the biomarker shows some relation to the architecture patterns. Citation Format: Florian J. Song, Alma Andoni, Manal Kordahi, Sara Batelli, Armin Meier, Markus Schick, Emilie Mahieu, Günter Schmidt, Claire E. Myers, Michael Abadier, Abjihit Dasgupta, Christopher Abbosh, Darren Hodgson, Michèle Orain, Fabien C. Lamaze, Yohan Bossé, Philippe Joubert. Identifying poor prognosis stage I lung adenocarcinoma patients through novel morphological biomarker based on computational pathology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6176.

Using Convolutional Neural Networks (CNN) to Realize Deep Recognition Analysis of Power Technology Standard Images

This article mainly studies the method of deep recognition analysis of power technology standard images using Convolutional Neural Network (CNN). In this study, an efficient CNN model was constructed to quickly and accurately classify and recognize common technical standard images in the power industry. The experimental results indicate that the research method has high accuracy and good practicality in the recognition of power technology standard images. In the experimental stage, this study designed four experiments to explore the application of CNN models in power technology standard image recognition, with a focus on evaluating the impact of different network designs and data processing strategies on model performance. In the benchmark model comparison experiment, the AUC (Area Under the ROC Curve) value of the CNN model in the power technology standard image recognition task was 0.96, the AUC value of the Support Vector Machine (SVM) was 0.88, and the AUC value of the decision tree model was 0.82. In experiments on the impact of different data augmentation strategies, the CNN model using scaling augmentation strategy achieved an accuracy of 90%. In the experiment on the impact of network depth on performance, the accuracy of the CNN model was highest improved to 95%. In real-time recognition capability testing, although the CNN model has the highest accuracy, the average processing time for each image is also relatively long. In the above data conclusion, the effectiveness of the CNN model in power technology standard image recognition tasks has been demonstrated, and possible paths for optimizing model performance in practical applications have also been revealed.

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI.

The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

Field camera input to virtual phantom (ViP) scanner acquisitions for quality assurance of derived MRI quantities: first implementation and proof-of-principle.

Quality assurance (QA) of measurements derived from MRI can require complicated test phantoms. This work introduces a new QA concept using gradient and transmit RF recordings by a limited field camera (FC) to govern the previous Virtual Phantom (ViP) method. The purpose is to describe the first technical implementation of combined FC+ViP, and illustrate its performance in examples, including quantitative first-pass myocardial perfusion. The new QA concept starts with a synthetic test object (STO) representing some arbitrary test input. Using recordings of the unmodified standard sequence by a gradient and RF waveform camera (FC), ViP calculates by Bloch simulation the continuous RF signal emitted by the STO during this sequence (hence FC+ViP). During nominally identical repetition of the sequence acquisition, ViP transmits the RF signal for scanner reception, reconstruction and any further parametric derivations by the unmodified standard scanner image reconstruction and analysis software. The scanner outputs were compared against the input STOs. First proof-of-principle was discussed and supported by correlation between scanner outputs and the input STO. The work makes no claim that its examples are valid QA methods. It concludes by proposing a new industrial standard for QA without the FC.

Analysis of Matrix Metalloproteinase Activity and Inhibition in Cancer Spheroids.

The utilization of tumor spheroids and organoids has greatly facilitated mechanistic understanding of tumor growth and invasion and lead to more effective high-throughput analysis of potential chemotherapeutic agents. In spheroid and organoid systems, tumor invasion occurs in three dimensions and monitoring this behavior can be data intensive. Quantitative correlation of tumor invasion with protease activity can further exacerbate data storage issues. The present method utilizes the "Hit Pick" approach to provide quantitative analysis and correlation of tumor invasion and membrane type 1 matrix metalloproteinase (MT1-MMP) activity in a rapid fashion with greatly reduced data storage requirements compared with standard image analysis approaches. Inhibition of MT1-MMP activity in spheroids can also be monitored by the present approach.

Manifold projection image segmentation for nano-XANES imaging

As spectral imaging techniques are becoming more prominent in science, advanced image segmentation algorithms are required to identify appropriate domains in these images. We present a version of image segmentation called manifold projection image segmentation (MPIS) that is generally applicable to a broad range of systems without the need for training because MPIS uses unsupervised machine learning with a few physically motivated hyperparameters. We apply MPIS to nanoscale x-ray absorption near edge structure (XANES) imaging, where XANES spectra are collected with nanometer spatial resolution. We show the superiority of manifold projection over linear transformations, such as the commonly used principal component analysis (PCA). Moreover, MPIS maintains accuracy while reducing computation time and sensitivity to noise compared to the standard nano-XANES imaging analysis procedure. Finally, we demonstrate how multimodal information, such as x-ray fluorescence data and spatial location of pixels, can be incorporated into the MPIS framework. We propose that MPIS is adaptable for any spectral imaging technique, including scanning transmission x-ray microscopy, where the length scale of domains is larger than the resolution of the experiment.

Abstract 17987: The Added Value of Radiomics Texture Features on Late Gadolinium Enhancement Cardiac Magnetic Resonance to Detect Myocardial Inflammation in Patients With Cardiac Sarcoidosis

Introduction: Radiomics analysis provides quantitative pixel-signal-intensity information on cardiac magnetic resonance (CMR). Hypothesis: Can radiomics improve performance of CMR for detection of myocardial inflammation in cardiac sarcoidosis (CS)? We aim to assess the added value of CMR radiomics to standard CMR image analysis for the diagnosis of active CS. Methods: We retrospectively analyzed CS patients referred for both CMR and F18-FDG-PET/CT(PET) within 90 days of each other. Myocardium was segmented on a dedicated semi-automated workflow by MIM encore software and features extracted by pyradiomics. PET were reviewed for the presence of myocardial FDG uptake, defining segments with inflammation. CMR-segments were reviewed for the presence of late gadolinium enhancement(LGE) and increased T2 signal by 2 experienced radiologists. Analysis was performed to assess for differences in radiomics features in segments with/without inflammation. ROC analysis was conducted to define the added value of radiomics for diagnosis of myocardial inflammation. Results: In 80 patients with 1360 AHA-CMR segments (mean age 58 years, 62.5% male) with CS, 276(20.3%) segments had FDG uptake on PET/CT, 252(18.5%) segments showed LGE and 54(3.9%) segments had increased signal on T2W images. Significant quantitative differences for LGE-derived radiomics features were highest for Entropy and Gray Level Cooccurrence Matrix Autocorrelation between segments with and without inflammation on PET (Table1). The presence of LGE was the best independent predictor for inflammation on PET. T2W abnormality alone was not an independent predictor from the presence of LGE. Significant improvement was seen in area under the curve when radiomics features were combined with standard CMR predictors (LGE/T2W) vs standard CMR predictors alone. Conclusions: Quantitative radiomics features on LGE-CMR have the potential to improve the ability of CMR for assessment of inflammation in CS.

Characterization of Anthropometric Changes in Patients with Relapsed/Refractory Myeloma Treated with BCMA Bispecific Antibody Teclistamab

Introduction: Cancer-associated cachexia is associated with poor survival rates in several cancer types. Multiple Myeloma (MM) preferentially occurs in the elderly and is the most common cancer that causes lytic bone metastasis causing bone pain, fractures, mobility limitations, and falls. Cachexia is projected to be common in myeloma but has not been fully studied. Myeloma treatments including corticosteroids, radiation and alkylators cause muscle loss, while novel immunotherapy is tumor-specific but their impact on cachexia is unknown. We characterize the changes in body composition in patients with relapsed/refractory myeloma treated with BCMA bispecific antibody teclistamab and determine the association between body composition with treatement response and overall survival. Methods: 49 MM patients treated with teclistamab between 2000 and 2021 were included. All were with relapsed/refractory disease after > 4 lines of therapy and received an approved dose and schedule of teclistamab. The response assessment was according to IMWG criteria. Body weight, body mass index (BMI), tumor burden, and organ function were assessed at baseline and post-treatment. Standard-of-care abdominopelvic CT or PET/CT scans at baseline and at last known follow up were analyzed with Data Analysis Facilitation Suite (DAFS) software to quantify skeletal muscle (SKM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). Differences between groups were tested using the χ 2 test or paired t-test. Progression free survival (PFS) and overall survival (OS) from the start of teclistamab to progression and death or last follow-up, respectively, were analyzed by the Kaplan-Meier method. Comparisons were made with the log-rank test. Results: Patient characteristics are detailed in Table 1. Based on the WHO definition, 2% were underweight, 16% were normal weight, 19% were overweight and 13% were obese, at baseline. The baseline mean SKM was 105.6 ± 27.8 (Male 122.0 ± 25.3, female 93.4 ± 23.5). SAT and VAT varied widely. Median SAT was 196.5 (range 48.3-558.6), and median VAT was 136.6 (33.09-448.1). Of 32 patients in the obese group, 16 (50%) patients had SKM that was below the mean SKM of the group. Baseline SAT and VAT, but not SKM were correlated with BMI. Twenty-seven of the 49 patients had paired imaging. The absolute changes in skeletal muscle and adipose tissue compartments are shown in Table 2. A majority of patients had a decline in SKM, SAT and VAT post-treatment: mean SKM decrease was 8.9 cm 2 ( p ≤ 0.01), mean SAT decreased 24.3 cm 2 ( p = 0.02) and mean VAT decreased 27.4 cm 2 ( p = 0.03), while BMI and weight were unchanged. Nineteen of 27 (70%) subjects responded to therapy. The changes in SKM, VAT, and SAT loss did not differ significantly between responders and non-responders. 38.8% of patients (19/49) had died by the end of the follow-up. Median follow-up duration was 2.8 years, overall survival was 1.7 years and PFS was 0.9 years. Baseline SKM, SAT, and VAT did not correlate with PFS or OS, but a decline in SMK significantly correlated with a shorter OS (p=0.04); 7/18 patients with decreased SKM and 2/9 patients with unchanged or increased SKM died. Response correlated strongly with overall survival (p=0.002). Conclusions: Body composition analysis of standard CT images may provide clinically relevant information for patients with advanced myeloma. Sarcopenia was common despite high BMI, and a further decline in skeletal muscle mass correlated with shorter survival, regardless of response to tumor-specific immunotherapy. Anthropometric changes should be validated in larger patient cohorts and included in future clinical trials. Further efforts should focus on the maintenance of muscle and visceral adipose tissue in this patient population.

A Novel Standardized Collaborative Online Model for Processing and Analyzing Remotely Sensed Images in Geographic Problems

In recent years, remote sensing image processing technology has developed rapidly, and the variety of remote sensing images has increased. Solving a geographic problem often requires multiple remote sensing images to be used together. For an image processing analyst, it is difficult to become proficient in the image processing of multiple types of remote sensing images. Therefore, it is necessary to have multiple image processing analysts collaborate to solve geographic problems. However, as a result of the naturally large volumes of data and the computer resources they consume for analysis, remote sensing images present a barrier in the collaboration of multidisciplinary remote sensing undertakings and analysts. As a result, during the development of the collaborative analysis process, it is necessary to achieve the online processing and analysis of remote sensing images, as well as to standardize the online remote sensing image collaborative analysis process. To address the above issues, a hierarchical collaborative online processing and analysis framework was developed in this paper. This framework defined a clear collaborative analysis structure, and it identifies what kinds of online image processing and analysis activities participants can engage in to successfully conduct collaborative processes. In addition, a collaborative process construction model and an online remote sensing image processing analysis model were developed to assist participants in creating a standard collaborative online image processing and analysis process. In order to demonstrate the feasibility and effectiveness of the framework and model, this paper developed a collaborative online post-disaster assessment process that utilizes radar images and optical remote sensing images for a real forest fire event. This process was based on the BPMN2.0 and OGC dual standards. Based on the results, the proposed framework provides a hierarchical collaborative remote sensing image processing and analysis process with well-defined stages and activities to guide the participants’ mutual collaboration. Additionally, the proposed model can help participants to develop a standardized collaborative online image processing process in terms of process structure and information interactions.

Evaluating the feasibility of measuring planar area of Sclerophytum penghuense and Cladiella hartogi using 2D image analysis

Soft coral (Octocorallia: Alcyonacea) farming has been widely used to decrease the fishing pressure on wild populations harvested for use in the ornamental trade and studies related to natural products. With a soft and flexible body, only a few attempts have been made to quantify the growth rate, either in the field or in aquarium systems. In this study, the planar area measurement approach was applied and evaluated for its applicability to soft corals. All fragments of Sclerophytum penghuense and Cladiella hartogi were placed on individual tiles (10 × 10 cm) and randomly distributed in the mesocosm. Two treatments were applied: (1) finger touch treatment, and (2) air exposure for one minute to avoid the influence of morphological flexibility caused by the hydroskeleton before taking photographs underwater. Two independent tests were carried out including (1) applying two treatments before taking photographs for four sides (only for S. penghuense), and (2) comparing the planar area and linear measurement with its dry weight (for both species). Since the shape of C. hartogi is stable through time, the first test was not required for C. hartogi. The photographs were taken twice per day (11:00am and 8:00pm), and five pictures were taken for each side. The results showed that there was no significant difference between one and five pictures used (P > 0.05). Furthermore, taking photographs of two sides that are perpendicular to each other is recommended to provide sufficient statistical power. Meanwhile, the air treatment made the fragments less flexible during the photographing process which should be used in this standardized 2D image analysis. Our results also showed that planar area was strongly correlated (P < 0.001) with its dry weight (R2 = 0.929 for S. penghuense and R2 = 0.945 for C. hartogi). In addition, the air exposure treatment was consistently similar during both times (daytime and nighttime). Based on the results, we suggest this standardized 2D method to measure the growth of soft corals by measuring planar area changes through time.

Combining RNAscope and immunohistochemistry to visualize inflammatory gene products in neurons and microglia.

A challenge for central nervous system (CNS) tissue analysis in neuroscience research has been the difficulty to codetect and colocalize gene and protein expression in the same tissue. Given the importance of identifying gene expression relative to proteins of interest, for example, cell-type specific markers, we aimed to develop a protocol to optimize their codetection. RNAscope fluorescent in situ hybridization (FISH) combined with immunohistochemistry (IHC) in fixed (CNS) tissue sections allows for reliable quantification of gene transcripts of interest within IHC-labeled cells. This paper describes a new method for simultaneous visualization of FISH and IHC in thicker (14-μm), fixed tissue samples, using spinal cord sections. This method's effectiveness is shown by the cell-type-specific quantification of two genes, namely the proinflammatory cytokine interleukin-1beta (IL-1b) and the inflammasome NLR family pyrin domain containing 3 (NLRP3). These genes are challenging to measure accurately using immunohistochemistry (IHC) due to the nonspecificity of available antibodies and the hard-to-distinguish, dot-like visualizations of the labeled proteins within the tissue. These measurements were carried out in spinal cord sections after unilateral chronic constriction injury of the sciatic nerve to induce neuroinflammation in the spinal cord. RNAscope is used to label transcripts of genes of interest and IHC is used to label cell-type specific antigens (IBA1 for microglia, NeuN for neurons). This combination allowed for labeled RNA transcripts to be quantified within cell-type specific boundaries using confocal microscopy and standard image analysis methods. This method makes it easy to answer empirical questions that are intractable with standard IHC or in situ hybridization alone. The method, which has been optimized for spinal cord tissue and to minimize tissue preparation time and costs, is described in detail from tissue collection to image analysis. Further, the relative expression changes in inflammatory genes NLRP3 and IL-1b in spinal cord microglia vs. neurons of somatotopically relevant laminae are described for the first time.

White Matter Microstructural Differences between Essential Tremor and Parkinson Disease, Evaluated Using Advanced Diffusion MRI Biomarkers.

Essential tremor (ET) is a common slowly-progressive neurologic disorder. It is predominantly characterized by kinetic tremors involving bilateral upper limbs. Although ET shares motor similarities with Parkinson disease (PD), there is no known relationship between ET and PD. We studied white matter differences between 17 ET and 68 PD patients using standard diffusion tensor imaging and fixel-based analysis (FBA). Diffusion magnetic resonance imaging data were acquired from two scanners (General Electric (GE) and Philips) with different numbers of diffusion directions. Fractional anisotropy maps were generated by the Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL), and FBA was performed using MRtrix3 to obtain fiber density, fiber bundle, and fiber density bundle cross-section. Compared with PD, significantly lower values of fiber density, fiber bundle, and fiber density bundle cross-section were found in the corpus callosum and left tapetum of the ET group. Additionally, significantly lower functional anisotropy values were found in the ET compared to the PD group, principally in the corpus callosum, corona radiata, and cingulum. In conclusion, differences in white matter integrity between ET and PD were observed by both FBA-based metrics and diffusion tensor imaging. Advanced diffusion-based metrics may provide a better understanding of the white matter microstructural characteristics in disparate motor-associated diseases with different underlying phenotypes, such as ET and PD.

SliDL: A toolbox for processing whole-slide images in deep learning.

The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists. However, WSIs present a number of unique challenges for analysis, requiring special consideration of image annotations, slide and image artefacts, and evaluation of WSI-trained model performance. Here we introduce SliDL, a Python library for performing pre- and post-processing of WSIs. SliDL makes WSI data handling easy, allowing users to perform essential processing tasks in a few simple lines of code, bridging the gap between standard image analysis and WSI analysis. We introduce each of the main functionalities within SliDL: from annotation and tile extraction to tissue detection and model evaluation. We also provide 'code snippets' to guide the user in running SliDL. SliDL has been designed to interact with PyTorch, one of the most widely used deep learning libraries, allowing seamless integration into deep learning workflows. By providing a framework in which deep learning methods for WSI analysis can be developed and applied, SliDL aims to increase the accessibility of an important application of deep learning.

Multiple sclerosis lesions that impair memory map to a connected memory circuit.

Nearly 1 million Americans are living with multiple sclerosis (MS) and 30-50% will experience memory dysfunction. It remains unclear whether this memory dysfunction is due to overall white matter lesion burden or damage to specific neuroanatomical structures. Here we test if MS memory dysfunction is associated with white matter lesions to a specific brain circuit. We performed a cross-sectional analysis of standard structural images and verbal memory scores as assessed by immediate recall trials from 431 patients with MS (mean age 49.2 years, 71.9% female) enrolled at a large, academic referral center. White matter lesion locations from each patient were mapped using a validated algorithm. First, we tested for associations between memory dysfunction and total MS lesion volume. Second, we tested for associations between memory dysfunction and lesion intersection with an a priori memory circuit derived from stroke lesions. Third, we performed mediation analyses to determine which variable was most associated with memory dysfunction. Finally, we performed a data-driven analysis to derive de-novo brain circuits for MS memory dysfunction using both functional (n = 1000) and structural (n = 178) connectomes. Both total lesion volume (r = 0.31, p < 0.001) and lesion damage to our a priori memory circuit (r = 0.34, p < 0.001) were associated with memory dysfunction. However, lesion damage to the memory circuit fully mediated the association of lesion volume with memory performance. Our data-driven analysis identified multiple connections associated with memory dysfunction, including peaks in the hippocampus (T = 6.05, family-wise error p = 0.000008), parahippocampus, fornix and cingulate. Finally, the overall topography of our data-driven MS memory circuit matched our a priori stroke-derived memory circuit. Lesion locations associated with memory dysfunction in MS map onto a specific brain circuit centered on the hippocampus. Lesion damage to this circuit fully mediated associations between lesion volume and memory. A circuit-based approach to mapping MS symptoms based on lesions visible on standard structural imaging may prove useful for localization and prognosis of higher order deficits in MS.

Pearls and Pitfalls of Adaptive Optics Ophthalmoscopy in Inherited Retinal Diseases.

Adaptive optics (AO) retinal imaging enables individual photoreceptors to be visualized in the clinical setting. AO imaging can be a powerful clinical tool for detecting photoreceptor degeneration at a cellular level that might be overlooked through conventional structural assessments, such as spectral-domain optical coherence tomography (SD-OCT). Therefore, AO imaging has gained significant interest in the study of photoreceptor degeneration, one of the most common causes of inherited blindness. Growing evidence supports that AO imaging may be useful for diagnosing early-stage retinal dystrophy before it becomes apparent on fundus examination or conventional retinal imaging. In addition, serial AO imaging may detect structural disease progression in early-stage disease over a shorter period compared to SD-OCT. Although AO imaging is gaining popularity as a structural endpoint in clinical trials, the results should be interpreted with caution due to several pitfalls, including the lack of standardized imaging and image analysis protocols, frequent ocular comorbidities that affect image quality, and significant interindividual variation of normal values. Herein, we summarize the current state-of-the-art AO imaging and review its potential applications, limitations, and pitfalls in patients with inherited retinal diseases.

Examination of embolisms in maple and birch saplings utilising microCT

We demonstrate the application of synchrotron x-ray microtomography (microCT) to non-invasively examine the internal structure of a maple and birch sapling. We show that, through the use of standard image analysis techniques, embolised vessels can be extracted from reconstructed slices of the stem. By combining these thresholded images with connectivity analysis, we map out the embolisms within the sapling in three dimensions and evaluate the size distribution, showing that large embolisms over 0.005mm3 in volume compose the majority of the saplings' total embolised volume. Finally we evaluate the radial distribution of embolisms, showing that in maple fewer embolisms are present towards the cambium, while birch has a more uniform distribution.

A Novel Hydrogel-Based 3D In Vitro Tumor Panel of 30 PDX Models Incorporates Tumor, Stromal and Immune Cell Compartments of the TME for the Screening of Oncology and Immuno-Therapies.

Human-relevant systems that mimic the 3D tumor microenvironment (TME), particularly the complex mechanisms of immuno-modulation in the tumor stroma, in a reproducible and scalable format are of high interest for the drug discovery industry. Here, we describe a novel 3D in vitro tumor panel comprising 30 distinct PDX models covering a range of histotypes and molecular subtypes and cocultured with fibroblasts and PBMCs in planar (flat) extracellular matrix hydrogels to reflect the three compartments of the TME-tumor, stroma, and immune cells. The panel was constructed in a 96-well plate format and assayed tumor size, tumor killing, and T-cell infiltration using high-content image analysis after 4 days of treatment. We screened the panel first against the chemotherapy drug Cisplatin to demonstrate feasibility and robustness, and subsequently assayed immuno-oncology agents Solitomab (CD3/EpCAM bispecific T-cell engager) and the immune checkpoint inhibitors (ICIs) Atezolizumab (anti-PDL1), Nivolumab (anti-PD1) and Ipilimumab (anti-CTLA4). Solitomab displayed a strong response across many PDX models in terms of tumor reduction and killing, allowing for its subsequent use as a positive control for ICIs. Interestingly, Atezolizumab and Nivolumab demonstrated a mild response compared to Ipilimumab in a subset of models from the panel. We later determined that PBMC spatial proximity in the assay setup was important for the PD1 inhibitor, hypothesizing that both duration and concentration of antigen exposure may be critical. The described 30-model panel represents a significant advancement toward screening in vitro models of the tumor microenvironment that include tumor, fibroblast, and immune cell populations in an extracellular matrix hydrogel, with robust and standardized high content image analysis in a planar hydrogel. The platform is aimed at rapidly screening various combinations and novel agents and forming a critical conduit to the clinic, thus accelerating drug discovery for the next generation of therapeutics.