Image Data Research Articles

Novel systematic processing of cardiac magnetic resonance imaging identifies target regions associated with infarct-related ventricular tachycardia

Abstract Background and Aims There is lack of agreement on late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging processing for guiding ventricular tachycardia (VT) ablation. We aim at developing and validating a systematic processing approach on LGE-CMR images to identify VT corridors that contain critical VT isthmus sites. Methods Translational study including 18 pigs with established myocardial infarction and inducible VT undergoing in vivo characterization of the anatomical and functional myocardial substrate associated with VT maintenance. Clinical validation was conducted in a multicenter series of 33 patients with ischemic cardiomyopathy undergoing VT ablation. Three-dimensional CMR-LGE images were processed using systematic scanning of 15 signal intensity (SI) cut-off ranges to obtain surface visualization of all potential VT corridors. Analysis and comparisons of imaging and electrophysiological data were performed in individuals with full electrophysiological characterization of the isthmus sites of at least one VT morphology. Results In both the experimental pig model and patients undergoing VT ablation, all the electrophysiologically-defined isthmus sites (n=11 and n=19, respectively) showed overlapping regions with CMR-based potential VT corridors. Such imaging-based VT corridors were less specific than electrophysiologically-guided ablation lesions at critical isthmus sites. However, an optimized strategy using the 7 most relevant SI cut-off ranges among patients showed an increase in specificity compared to using 15 SI cut-off ranges (70% vs 62%, respectively), without diminishing the capability to detect VT isthmus sites (sensitivity 100%). Conclusions Systematic imaging processing of LGE-CMR sequences using several SI cut-off ranges may improve and standardize procedure planning to identify VT isthmus sites.

Interpretation of SD-OCT imaging data in real-life conditions versus standardized reading centre analysis in eyes with diabetic macular oedema or macular oedema secondary to retinal vein occlusion: 24-month follow-up of the ORCA study.

As part of the prospective, non-interventional OCEAN study, the ORCA module evaluated physicians' spectral domain optical coherence tomography (SD-OCT) image interpretations in the treatment of diabetic macular oedema (DME) or macular oedema (ME) secondary to retinal vein occlusion (RVO). Presence of intraretinal fluid (IRF) and/or subretinal fluid (SRF) was evaluated independently by physicians and reading centres (RCs) on 1612 SD-OCT scans of 133 patients diagnosed with either DME or ME secondary to RVO. Agreement between physicians and RCs was calculated for both cohorts individually and as a combined ME cohort. Physicians' treatment decisions were analysed related to the results of the OCT-evaluations. For the combined ME cohort, presence of IRF/SRF was recorded by RCs in 792/1612 (49.1%) visits and by physicians in 852/1612 (52.9%) visits, with an agreement regarding presence or absence of foveal fluid in 70.4% of cases. In 64.4% (510/792) of visits with RC-detected foveal IRF and/or SRF no injection was given. In 30.3% of these visits with foveal fluid no reason was identified for a 'watch and wait' approach indicating possible undertreatment. BCVA deterioration was seen in a quarter of these eyes at the following visit. Despite good agreement between physicians and RCs to recognize SRF and IRF, our data indicate that omitting injections despite foveal involvement of fluid is frequent in routine clinical practice. This may put patients at risk of undertreatment, which may negatively impact real-life BCVA outcomes. www. gov , identifier NCT02194803.

Opportunity and Opportunism in Artificial-Intelligence-Powered Data Extraction: A Value-Centered Approach.

Radiologists' traditional role in the diagnostic process is to respond to specific clinical questions and reduce uncertainty enough to permit treatment decisions. This charge is rapidly evolving due to forces such as artificial intelligence [AI], big data [opportunistic imaging, imaging prognostication], and advanced diagnostic technologies. A new "modernistic" paradigm is emerging whereby radiologists, in conjunction with computer algorithms, will be tasked with extracting as much information from imaging data as possible, often without a specific clinical question being posed and independent of any stated clinical need. In addition, AI algorithms are increasingly able to predict long-term outcomes using data from seemingly normal examinations, enabling AI-assisted prognostication. As these algorithms become a standard component of radiology practice, the sheer amount of information they demand will increase the need for streamlined workflows, communication, and data management techniques. In addition, the provision of such information raises reimbursement, liability, and access issues. Guidelines will be needed to ensure all patients have access to the benefits of this new technology and guarantee mined data do not inadvertently create harm. In this article, we discuss challenges and opportunities relevant to radiologists in this changing landscape, with an emphasis on ensuring that radiologists provide high-value care.

Evaluation of Intra- and Inter-Network Connectivity within Major Brain Networks in Drug-Resistant Depression Using rs-fMRI

Background: Major Depressive Disorder (MDD) is a significant challenge in modern medicine due to its unclear underlying causes. Brain network dysfunction is believed to play a key role in its pathophysiology. Resting-state functional MRI (rs-fMRI), a neuroimaging technique, enables the in vivo assessment of functional connectivity (FC) between brain regions, offering insights into these network dysfunctions. The aim of this study was to evaluate abnormalities in FC within major brain networks in patients with drug-resistant MDD. Methods: The study group consisted of 26 patients with drug-resistant MDD and an age-matched control group (CG) of 26 healthy subjects. The rs-fMRI studies were performed on a 3T MR scanner (Philips, Ingenia) using a 32-channel head and neck coil. Imaging data were statistically analyzed, focusing on the intra- and inter-network FC of the following networks: default mode (DMN), sensorimotor (SMN), visual (VN), salience (SN), cerebellar (CN), dorsal attention (DAN), language (LN), and frontoparietal (FPN). Results: In patients with MDD, the intra-network analysis showed significantly decreased FC between nodes within VN compared to CG. In contrast, the inter-network analysis showed significantly increased FC between nodes from VN and SN or VN and DAN compared to CG. Decreased FC was found between SN and CN or SN and FPN as well as VN and DAN nodes compared to CG. Conclusions: Patients with MDD showed significant abnormalities in resting-state cortical activity, mainly regarding inter-network functional connectivity. These results contribute to the knowledge on the pathomechanism of MDD and may also be useful for developing new treatments.

Automated segmentation of midbrain nuclei using deep learning and multisequence MRI: a longitudinal study on iron accumulation with age

Abstract Elevated levels of brain iron, particularly within the basal ganglia, have been associated with cognitive and motor impairment in normal aging and neurodegenerative conditions. The subthalamic nucleus (STN), substantia nigra (SN), and red nucleus (RN), despite their high iron content and contribution to motor and cognitive processes, are less frequently studied. This oversight can largely be attributed to the challenges posed by in-vivo assessments of these small, deep-seated midbrain structures. We developed and validated an automated tool for the segmentation of the STN, SN, and RN. Multi-sequence magnetic resonance imaging (MRI) data including T1-weighted, FLAIR, Quantitative Susceptibility Mapping (QSM) and R2* alongside manual delineation on QSM images of 40 individuals, was used to train segmentation models based on nnU-Net deep-learning framework. A combination of QSM and FLAIR sequences was found to be optimal for structure segmentation (mean Dice scores of 0.84, 0.91 and 0.94 for STN, SN and RN, respectively). We next applied the automated segmentation method to an independent 3-year longitudinal dataset including 175 healthy adults (age range at baseline: 20-79 years old). Structural equation modelling was used to assess iron accumulation over time using age, sex, baseline iron and regional volume as factors of interest. Cross-sectionally, older age was linearly associated with higher iron load in SN and STN; the association was non-linear in RN. Longitudinally, results indicated significant iron accumulation in the STN (Mean increase = 0.02, p = 0.005) and SN (Mean increase = 0.035, p = 0.001), but not in the RN (Mean increase = 0.015, p = 0.2). Our findings demonstrated high performance of nnU-Net in automated segmentation, and advanced our understanding of iron accumulation in midbrain nuclei in aging.

Advancements in UAV Path Planning: A Deep Reinforcement Learning Approach with Soft Actor-Critic for Enhanced Navigation

This paper tackles the intricate challenge of autonomous navigation for Unmanned Aerial Vehicles (UAVs) through dynamically changing environments. We focus on a sophisticated Deep Reinforcement Learning (DRL) approach using the Soft Actor-Critic (SAC) algorithm, optimized for UAV path planning within a continuous action space. This methodology leverages environmental image data to enhance the precision of flight maneuvers and effective obstacle avoidance. Our approach, validated through extensive simulations in Gazebo and field tests, demonstrates the algorithm’s efficacy in enabling UAVs to adeptly navigate obstacles using depth maps. The study further explores the robustness of the SAC algorithm by comparing it with traditional DRL methods, emphasizing its superior performance in real-world applications. This research contributes significantly to advancing UAV technology, particularly in autonomous motion planning, by integrating cutting-edge machine learning techniques. The video link is: https://www.youtube.com/watch?v=Nd_aMzejNXY .

Assessing the nonlinear impact of green space exposure on psychological stress perception using machine learning and street view images

IntroductionUrban green space (GS) exposure is recognized as a nature-based strategy for addressing urban challenges. However, the stress relieving effects and mechanisms of GS exposure are yet to be fully explored. The development of machine learning and street view images offers a method for large-scale measurement and precise empirical analysis.MethodsThis study focuses on the central area of Shanghai, examining the complex effects of GS exposure on psychological stress perception. By constructing a multidimensional psychological stress perception scale and integrating machine learning algorithms with extensive street view images data, we successfully developed a framework for measuring urban stress perception. Using the scores from the psychological stress perception scale provided by volunteers as labeled data, we predicted the psychological stress perception in Shanghai's central urban area through the Support Vector Machine (SVM) algorithm. Additionally, this study employed the interpretable machine learning model eXtreme Gradient Boosting (XGBoost) algorithm to reveal the nonlinear relationship between GS exposure and residents' psychological stress.ResultsResults indicate that the GS exposure in central Shanghai is generally low, with significant spatial heterogeneity. GS exposure has a positive impact on reducing residents' psychological stress. However, this effect has a threshold; when GS exposure exceeds 0.35, its impact on stress perception gradually diminishes.DiscussionWe recommend combining the threshold of stress perception with GS exposure to identify urban spaces, thereby guiding precise strategies for enhancing GS. This research not only demonstrates the complex mitigating effect of GS exposure on psychological stress perception but also emphasizes the importance of considering the “dose-effect” of it in urban planning and construction. Based on open-source data, the framework and methods developed in this study have the potential to be applied in different urban environments, thus providing more comprehensive support for future urban planning.

Sequence Segmentation of Nematodes in Atlantic Cod with Multispectral Imaging Data

Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality.

Gastrointestinal image stitching based on improved unsupervised algorithm.

Image stitching is a traditional but challenging computer vision task. The goal is to stitch together multiple images with overlapping areas into a single, natural-looking, high-resolution image without ghosts or seams. This article aims to increase the field of view of gastroenteroscopy and reduce the missed detection rate. To this end, an improved depth framework based on unsupervised panoramic image stitching of the gastrointestinal tract is proposed. In addition, preprocessing for aberration correction of monocular endoscope images is introduced, and a C2f module is added to the image reconstruction network to improve the network's ability to extract features. A comprehensive real image data set, GASE-Dataset, is proposed to establish an evaluation benchmark and training learning framework for unsupervised deep gastrointestinal image splicing. Experimental results show that the MSE, RMSE, PSNR, SSIM and RMSE_SW indicators are improved, while the splicing time remains within an acceptable range. Compared with traditional image stitching methods, the performance of this method is enhanced. In addition, improvements are proposed to address the problems of lack of annotated data, insufficient generalization ability and insufficient comprehensive performance in image stitching schemes based on supervised learning. These improvements provide valuable aids in gastrointestinal examination.

Precision estimation of winter wheat crop height and above-ground biomass using unmanned aerial vehicle imagery and oblique photoghraphy point cloud data

IntroductionCrop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural production. However, traditional data acquisition methods suffer from drawbacks such as time-consuming, laborious and destructive sampling.MethodsThe current approach to estimating AGB using unmanned aerial vehicles (UAVs) remote sensing relies solely on spectral data, resulting in low accuracy in estimation. This method fails to address the ill-posed inverse problem of mapping from two-dimensional to three-dimensional and issues related to spectral saturation. To overcome these challenges, RGB and multispectral sensors mounted on UAVs were employed to acquire spectral image data. The five-directional oblique photography technique was utilized to construct the three-dimensional point cloud for extracting crop height.Results and DiscussionThis study comparatively analyzed the potential of the mean method and the Accumulated Incremental Height (AIH) method in crop height extraction. Utilizing Vegetation Indices (VIs), AIH and their feature combinations, models including Random Forest Regression (RFR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR) and Ridge Regression (RR) were constructed to estimate winter wheat AGB. The research results indicated that the AIH method performed well in crop height extraction, with minimal differences between 95% AIH and measured crop height values were observed across various growth stages of wheat, yielding R2 ranging from 0.768 to 0.784. Compared to individual features, the combination of multiple features significantly improved the model’s estimate accuracy. The incorporation of AIH features helps alleviate the effects of spectral saturation. Coupling VIs with AIH features, the model’s R2 increases from 0.694-0.885 with only VIs features to 0.728-0.925. In comparing the performance of five machine learning algorithms, it was discovered that models constructed based on decision trees were superior to other machine learning algorithms. Among them, the RFR algorithm performed optimally, with R2 ranging from 0.9 to 0.93.ConclusionIn conclusion, leveraging multi-source remote sensing data from UAVs with machine learning algorithms overcomes the limitations of traditional crop monitoring methods, offering a technological reference for precision agriculture management and decision-making.

Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.

Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective. We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables. A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance. Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.

Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease.

Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI). Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI. Our risk score holds great potential to improve the precision of early risk assessment. Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification.

A Cross Spatio-Temporal Pathology-based Lung Nodule Dataset

Recently, intelligent analysis of lung nodules with the assistant of computer aided diagnosis (CAD) techniques can improve the accuracy rate of lung cancer diagnosis. However, existing CAD systems and pulmonary datasets mainly focus on Computed Tomography (CT) images from one single period, while ignoring the cross spatio-temporal features associated with the progression of nodules contained in imaging data from various captured periods of lung cancer. If the evolution patterns of nodules across various periods in the patients’ CT sequences can be explored, it will play a crucial role in guiding the precise screening identification of lung cancer. Therefore, a cross spatio-temporal lung nodule dataset with pathological information for nodule identification and diagnosis is constructed, which contains 317 CT sequences and 2,295 annotated nodules from 109 patients. This comprehensive database is intended to drive research in the field of CAD towards more practical and robust methods, and also contribute to the further exploration of precision medicine related field. To ensure patient confidentiality, we have removed sensitive information from the dataset.

Frontoparietal network topology as a neural marker of musical perceptual abilities

Why are some individuals more musical than others? Neither cognitive testing nor classical localizationist neuroscience alone can provide a complete answer. Here, we test how the interplay of brain network organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a canonical working memory network encompassing prefrontal and posterior parietal regions. Using graph theory, we examine structural and functional frontoparietal network organization in relation to assessments of musical aptitude and experience. Results reveal a positive correlation between perceptual abilities and the integration efficiency of key frontoparietal regions. The linkage between functional networks and musical abilities is mediated by working memory processes, whereas structural networks influence these abilities through sensory integration. Our work lays the foundation for future investigations into the neurobiological roots of individual differences in musicality.

Identifying individual brain development using multimodality brain network

The cortical development of our brains is in a hierarchical manner and promotes the emergence of large-scale functional hierarchy. However, under interindividual heterogenicity, how the spatiotemporal features of brain networks reflect brain development and mental health remains unclear. Here we collect both resting-state electroencephalography and functional magnetic resonance imaging data from the Child Mind Institute Biobank to demonstrate that during brain growth, the global dynamic patterns of brain states become more active and the dominant networks shift from sensory to higher-level networks; the individual functional network patterns become more similar to that of adults and their spatial coupling tends to be invariable. Furthermore, the properties of multimodality brain networks are sufficiently robust to identify healthy brain age and mental disorders at specific ages. Therefore, multimodality brain networks provide new insights into the functional development of the brain and a more reliable and reasonable approach for age prediction and individual diagnosis.

Association between Neural Plasticity and Pain-Related Fear in Chronic Ankle Instability: A Structural Neuroimaging Study.

Pain-related movement fear is a contributing factor to residual pain and functional deficits in chronic ankle instability (CAI), but its underlying neural mechanisms remain unclear. We aimed to (1) delineate whether participants with CAI exhibit discernible differences in specific emotion and pain-related brain regions, compared to a healthy control (HC) cohort and (2) explore potential neural mechanisms underlying pain and fear in participants with CAI, with an emphasis on investigating possible associations with pain-related neural plasticity. Cross-sectional study. University research laboratory. 28 participants with CAI (17males and 11 females; age: 31.28±6.31 years) and 28 HCs (16 males and 12 females; age: 30.18±7.59 years). We analyzed T1 structural imaging data from participants and assessed their fear of movement and pain intensity using the Tampa Scale for Kinesiophobia (TSK) and the Visual Analog Scale (VAS) for pain, respectively. We compared the mean gray matter (GM) density of pain-related area between the two groups and their correlations with the TSK and VAS scores. In comparison with the HC group, participants with CAI showed a significant decrease in the mean GM density in the prefrontal cortex (Cohen's d = -0.808) and periaqueductal gray (Cohen's d = -0.934). In participants with CAI, the mean GM density of the prefrontal cortex (PFC) was negatively correlated with the TSK scores (r = -0.531). During intense exercise, the mean GM density of the periaqueductal gray (PAG) was negatively correlated with the VAS scores (r = -0.484). Additionally, TSK scores were positively correlated with VAS scores (r = 0.455). Our exploratory findings suggest that, in participants with CAI, the atrophy of the PFC and PAG may be associated with pain-related fear. Future clinical diagnosis and treatment for CAI should consider the impact of psychological barriers on functional recovery.

Detection rate of gastrin-releasing peptide receptor (GRPr) targeted tracers for positron emission tomography (PET) imaging in primary prostate cancer: a systematic review and meta-analysis.

The gastrin-releasing peptide receptor (GRPr) has gained recognition as a promising target for both diagnostic and therapeutic applications in a variety of human cancers. This study aims to explore the primary tumor detection capabilities of [68Ga] Ga-GRPr PET imaging, specifically in newly diagnosed intra-prostatic prostate cancer lesions (PCa). Following PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies) guidelines, a systematic literature search was conducted using the Medline, Embase, Scopus, and Web of Science databases. Data regarding patient characteristics and imaging procedure details-including the type of radiotracer used, administered activity, image acquisition time, scanner modality, criteria, and detection rate of index test-were extracted from the included studies. The pooled patient-and lesion-based detection rates, along with their corresponding 95% confidence intervals (CI), were calculated using a random effects model. The final analysis included 9 studies involving 291 patients and 350 intra-prostatic lesions with [68Ga] Ga-GRPr PET imaging in primary PCa. In per-patient-based analysis of [68Ga] Ga-GRPr PET imaging, the pooled detection rates of overall and patients with Gleason score ≥ 7 were 87.09% (95% CI 74.98-93.82) and 89.01% (95% CI 68.17-96.84), respectively. In per-lesion-based analysis, the pooled detection rate [68Ga] Ga-GRPr PET imaging was 78.54% (95% CI 69.8-85.29). The pooled detection rate mpMRI (multiparametric magnetic resonance imaging) in patient-based analysis was 91.85% (95% CI 80.12-96.92). The difference between the detection rates of the mpMRI and [68Ga] Ga-GRPr PET imaging was not statistically significant (OR 0.90, 95% CI 0.23-3.51). Our findings suggest that [68Ga] Ga-GRPr PET imaging has the potential as a diagnostic target for primary PCa. Future research is needed to determine the effectiveness of [68Ga] Ga-GRPr PET in delivering additional imaging data and guiding therapeutic decisions.

Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models

The task of localizing distinct anatomical structures in medical image data is an essential prerequisite for several medical applications, such as treatment planning in orthodontics, bone-age estimation, or initialization of segmentation methods in automated image analysis tools. Currently, Anatomical Landmark Localization (ALL) is mainly solved by deep-learning methods, which cannot guarantee robust ALL predictions; there may always be outlier predictions that are far from their ground truth locations due to out-of-distribution inputs. However, these localization outliers are detrimental to the performance of subsequent medical applications that rely on ALL results. The current ALL literature relies heavily on implicit anatomical constraints built into the loss function and network architecture to reduce the risk of anatomically infeasible predictions. However, we argue that in medical imaging, where images are generally acquired in a controlled environment, we should use stronger explicit anatomical constraints to reduce the number of outliers as much as possible. Therefore, we propose the end-to-end trainable Global Anatomical Feasibility Filter and Analysis (GAFFA) method, which uses prior anatomical knowledge estimated from data to explicitly enforce anatomical constraints. GAFFA refines the initial localization results of a U-Net by approximately solving a Markov Random Field (MRF) with a single iteration of the sum-product algorithm in a differentiable manner. Our experiments demonstrate that GAFFA outperforms all other landmark refinement methods investigated in our framework. Moreover, we show that GAFFA is more robust to large outliers than state-of-the-art methods on the studied X-ray hand dataset. We further motivate this claim by visualizing the anatomical constraints used in GAFFA as spatial energy heatmaps, which allowed us to find an annotation error in the hand dataset not previously discussed in the literature.

Machine Learning for Deconvolution and Segmentation of Hyperspectral Imaging Data from Biopharmaceutical Resins.

Biopharmaceutical resins are pivotal inert matrices used across industry and academia, playing crucial roles in a myriad of applications. For biopharmaceutical process research and development applications, a deep understanding of the physical and chemical properties of the resin itself is frequently required, including for drug purification, drug delivery, and immobilized biocatalysis. Nevertheless, the prevailing methodologies currently employed for elucidating these important aspects of biopharmaceutical resins are often lacking, frequently require significant sample alteration, are destructive or ionizing in nature, and may not adequately provide representative information. In this work, we propose the use of unsupervised machine learning technologies, in the form of both non-negative matrix factorization (NMF) and k-means segmentation, in conjugation with Raman hyperspectral imaging to rapidly elucidate the molecular and spatial properties of biopharmaceutical resins. Leveraging our proposed technology, we offer a new approach to comprehensively understanding important resin-based systems for application across biopharmaceuticals and beyond. Specifically, focusing herein on a representative resin widely utilized across the industry (i.e., Immobead 150P), our findings showcase the ability of our machine learning-based technology to molecularly identify and spatially resolve all chemical species present. Further, we offer a comprehensive evaluation of optimal excitation for hyperspectral imaging data collection, demonstrating results across 532, 638, and 785 nm excitation. In all cases, our proposed technology deconvoluted, both spatially and spectrally, resin and glass substrates via NMF. After NMF deconvolution, image segmentation was also successfully accomplished in all data sets via k-means clustering. To the best of our knowledge, this is the first report utilizing the combination of two unsupervised machine learning methodologies, combining NMF and k-means, for the rapid deconvolution and segmentation of biopharmaceutical resins. As such, we offer a powerful new data-rich experimentation tool for application across multidisciplinary fields for a deeper understanding of resins.

Two Patients With Ipsilateral Cerebellar Hemorrhage After Superficial Temporal Artery-Middle Cerebral Artery Bypass Grafting.

To summarize the clinical experience of ipsilateral cerebellar hemorrhage after superficial temporal artery-middle cerebral artery bypass surgery. The clinical data of 2 patients with cerebellar hemorrhage after superficial temporal artery-middle cerebral artery bypass grafting were retrospectively collected, including 1 case with left cerebellar hemorrhage after left cerebral artery bypass grafting and 1 case with right cerebellar hemorrhage after right cerebral artery bypass grafting. The perioperative blood pressure, laboratory, and imaging data were analyzed. All patients had a history of hypertension, and their perioperative blood pressure was stable. Low-density lipoprotein cholesterol and high-density lipoprotein cholesterol were normal before the operation. Cerebral magnetic resonance imaging showed cerebral atherosclerosis and multiple ischemic cerebral infarcts. At 24 hours after surgery, the patients' continuous epidural low drainage was 260 mL and 160 mL, respectively. The amount of cerebellar bleeding was small, and no new sequelae were left after conservative treatment with drugs. Cerebellar hemorrhage after superficial temporal artery-middle cerebral artery bypass grafting is related to perioperative blood pressure fluctuation, hemodynamic changes, hemorrhagic transformation of ischemic lesions, and excessive cerebrospinal fluid drainage. Maintaining stable blood pressure during the perioperative period and avoiding excessive and rapid loss of cerebrospinal fluid after operation can reduce the occurrence of this complication.