Intensity Normalization Research Articles

Chan-Vese aided fuzzy C-means approach for whole breast and fibroglandular tissue segmentation: Preliminary application to real-world breast MRI.

Magnetic resonance imaging (MRI) is a highly sensitive modality for diagnosing breast cancer, providing an expanding range of clinical usages that are crucial for the care of women at elevated risk of breast cancer development. Segmentation of the whole breast and fibroglandular tissue (FGT), used to evaluate breast cancer risk, is often manually delineated by radiologists in clinical practice. In this paper, we aim to substitute handcrafted breast density segmentation and categorization. The traditional fuzzy C-means (FCM) enable automatic segmentation but may be susceptible to heterogeneity or sparse FGT distribution in MRI. We develop a new automated technique for the segmentation of whole breast and FGT for the coronal-view MRI. We propose a Chan-Vese (CV) aided FCM segmentation approach for estimating the FGT in the whole breast using fat-suppressed (FS) precontrast T1-weighted breast MRI. We present a methodology pipeline comprising region-of-interest (ROI) extraction, nonparametric non-uniform intensity normalization N4 algorithm-based intensity inhomogeneity correction, skin-layer extraction, and then whole breast and FGT segmentation. Our approach involves the FCM algorithm to assign membership degrees to pixels, distinguishing FGT regions from surrounding adipose tissues by assessing their probability of belonging to specific FGT regions, and subsequently, the region-based active contour CV model leverages these membership degrees to direct contour evolution and enhance segmentation boundaries. The proposed method adeptly tackles common challenges in MRI, including blurred edges, low contrast, and intensity inhomogeneity, with efficiency. We evaluated our approach on the Duke Breast Cancer MRI data (DBCM-data) and achieved good segmentation accuracy in terms of Dice similarity coefficient (DSC), Intersection-over-Union (IoU), and Sensitivity (SEN). Our method demonstrates significant accuracy, achieving a DSC (%) of 93.2 ± 3.3 and 84.1 ± 4.9, IoU (%) of 86.4 ± 3.5 and 73.2 ± 5.1, and SEN 87.3 ± 4.1 and 76.7 ± 4.1 for the segmentations of whole breast and FGT, respectively. Our results demonstrated that the CV-aided FCM approach significantly outperformed the existing methods and resulted in significantly more accurate whole breast and FGT segmentation in MRI data.

Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet.

The diverse types and sizes, proximity to non-nodule structures, identical shape characteristics, and varying sizes of nodules make them challenging for segmentation methods. Although many efforts have been made in automatic lung nodule segmentation, most of them have not sufficiently addressed the challenges related to the type and size of nodules, such as juxta-pleural and juxta-vascular nodules. The current research introduces a Squeeze-Excitation Dilated Attention-based Residual U-Net (SEDARU-Net) with a robust intensity normalization technique to address the challenges related to different types and sizes of lung nodules and to achieve an improved lung nodule segmentation. After preprocessing the images with the intensity normalization method and extracting the Regions of Interest by YOLOv3, they are fed into the SEDARU-Net with dilated convolutions in the encoder part. Then, the extracted features are given to the decoder part, which involves transposed convolutions, Squeeze-Excitation Dilated Residual blocks, and skip connections equipped with an Attention Gate, to decode the feature maps and construct the segmentation mask. The proposed model was evaluated using the publicly available Lung Nodule Analysis 2016 (LUNA16) dataset, achieving a Dice Similarity Coefficient of 97.86%, IoU of 96.40%, sensitivity of 96.54%, and precision of 98.84%. Finally, it was shown that each added component to the U-Net's structure and the intensity normalization technique increased the Dice Similarity Coefficient by more than 2%. The proposed method suggests a potential clinical tool to address challenges related to the segmentation of lung nodules with different types located in the proximity of non-nodule structures.

Using the coefficient of determination to identify injury regions after stroke in pre-clinical FDG-PET images

Background:In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of the most appropriate normalization method in stroke studies remains unclear, as demonstrated by inconsistent findings in the literature. Materials and methods:Here, we propose a regression- and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination R2 was calculated to measure the linearity. The R2 values between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those with reduced linearity. Our method was compared with voxel-wise analysis under different intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data. Results:The significant regions identified by the proposed method showed a large degree of overlap with the infarcted regions identified by TTC data, as measured by the Dice similarity coefficient (DSC). The average DSC of the proposed method was 59.7%, whereas the DSCs of the existing approaches ranged from 41.4%∼51.3%. Additional validation using receiver operating characteristic (ROC) demonstrated that the area under the curve (AUC) of the average ROC curves reached 0.84 using the proposed method, whereas existing methods achieved AUCs ranging from 0.77∼0.79. The identified regions were consistent across the two independent data sets, and some findings were corroborated by other publications. Conclusions:The proposed model presents a novel quantitative approach for identifying injury regions post-stroke using FDG-PET images. The calculation does not require intensity normalization and can be applied to individual subjects. The method yields more sensitive results compared to existing identification methods.

Classification of Brain Tumor based on Machine Learning Algorithms: A Review

Brain tumor classification using machine learning algorithms is pivotal for medical diagnostics, particularly in magnetic resonance imaging (MRI) analysis. This review provides a comprehensive overview of recent advancements in brain tumor classification methodologies, emphasizing preprocessing, feature extraction, and classification phases. Preprocessing steps involve noise reduction and intensity normalization, while feature extraction encompasses texture analysis and deep learning-based methods. Machine learning algorithms such as support vector machines (SVM) and convolutional neural networks (CNNs) are utilized for accurate classification based on extracted features. Recent literature highlights the significance of diverse datasets, hyperparameter tuning, and segmentation techniques in improving diagnostic accuracy. Noteworthy methodologies include deep learning models for glioma grading and novel optimization techniques for tumor segmentation. The review underscores interdisciplinary collaboration between medical professionals and computational scientists to refine existing methodologies and overcome challenges. A systematic search of academic databases, including PubMed, IEEE Xplore, ACM Digital Library, ScienceDirect, Springer and Google Scholar, was conducted. Continued evolution in brain tumor classification promises enhanced diagnostic accuracy and personalized treatment strategies, ultimately improving patient outcomes in neuro-oncology.

DCA-Enhanced Alzheimer's detection with shearlet and deep learning integration.

Alzheimer's dementia (AD) is a neurodegenerative disorder that affects the central nervous system, causing the cells to stop working or die. The quality of life for individuals with AD steadily declines over time. While current treatments can relieve symptoms, a definitive cure remains elusive. However, technological advancements in machine learning (ML) and deep learning (DL) have opened up new possibilities for early AD detection. Early diagnosis is crucial, as trial drugs show promising results in patients who are diagnosed early. This study used a magnetic resonance imaging (MRI) dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The dataset consisted of 200 patients who were followed up at different time points and categorized as having AD (50), progressive-mild cognitive impairment to AD (50), stable-mild cognitive impairment (50), or cognitively normal (50). However, the utilization of MRI datasets poses challenges such as high dimensionality, limited training samples, and variability within and between subjects. To overcome these challenges, I propose using convolutional neural networks (CNNs) to extract informative features from an MRI sample. I fine-tune four pretrained models (i.e., SqueezeNet-v1.1, MobileNet-v2, Xception, and Inception-v3) to generate discriminative descriptors of MRI sample characteristics. Additionally, I suggest using the 3D shearlet transform, considering the volumetric properties of MRI data. Before the transformation, I implemented preprocessing protocols such as skull stripping, normalization of image intensity, and spatial cropping. I then summarize the shearlet coefficients using texture-based techniques. Finally, I integrate both deep and shearlet-based features using discriminant correlation analysis (DCA) to yield a robust and computationally efficient classification model. I employ two classifiers, support vector machines (SVMs) and decision tree baggers (DTBs). My objective was to develop a model capable of accurately diagnosing early-stage AD that can facilitate effective intervention and management of the condition. Our feature representation demonstrated high accuracy when applied to AD datasets at three time points. Specifically, accuracies of 94.46%, 92.97%, and 95.44% were achieved 18 months, 12 months, and at the time of stable diagnosis, respectively.

Implementation of Automatic Segmentation Framework as Preprocessing Step for Radiomics Analysis of Lung Anatomical Districts

Background: The advent of artificial intelligence has significantly impacted radiology, with radiomics emerging as a transformative approach that extracts quantitative data from medical images to improve diagnostic and therapeutic accuracy. This study aimed to enhance the radiomic workflow by applying deep learning, through transfer learning, for the automatic segmentation of lung regions in computed tomography scans as a preprocessing step. Methods: Leveraging a pipeline articulated in (i) patient-based data splitting, (ii) intensity normalization, (iii) voxel resampling, (iv) bed removal, (v) contrast enhancement and (vi) model training, a DeepLabV3+ convolutional neural network (CNN) was fine tuned to perform whole-lung-region segmentation. Results: The trained model achieved high accuracy, Dice coefficient (0.97) and BF (93.06%) scores, and it effectively preserved lung region areas and removed confounding anatomical regions such as the heart and the spine. Conclusions: This study introduces a deep learning framework for the automatic segmentation of lung regions in CT images, leveraging an articulated pipeline and demonstrating excellent performance of the model, effectively isolating lung regions while excluding confounding anatomical structures. Ultimately, this work paves the way for more efficient, automated preprocessing tools in lung cancer detection, with potential to significantly improve clinical decision making and patient outcomes.

Deciphering the enigma of atypical forms of Alzheimer's disease by employing connectomics in the context of early and late‐phase Amyloid PET and [18F]FDG PET Imaging

AbstractBackgroundAlzheimer's disease (AD) is the most common form of dementia, predominantly manifesting as amnestic impairment. However, atypical presentations such as logopenic variant of primary progressive aphasia (lvPPA), posterior cortical atrophy (PCA), corticobasal syndrome (CBS) and frontal AD pose diagnostic challenges. This study presents preliminary data from a retrospective analysis investigating brain functional differences between typical and atypical AD forms. Specifically, a structural connectome, a comprehensive map of anatomical white matter connections in the brain, was employed alongside late‐phase amyloid [18F]FBB PET, which resembles focal regional amyloid uptake. Additionally, the research aims to examine whether early‐phase amyloid [18F]FBB PET can serve as a surrogate for glucose metabolism throughout various brain regions, similar to [18F]FDG PET.MethodThirty patients have been recruited, including 13 with atypical AD (median age 70; median MMSE 21) and 17 with typical amnestic phenotype (median age 71; median MMSE 23). Neuropsychological tests, 3D isotropic T1 MRI, [18F]FDG PET, and [18F]FBB PET (consisting of 3 early frames and 1 late frame) were performed for each subject. Structural and functional images underwent spatial registration, segmentation, and intensity normalization, using a custom Python pipeline based on FreeSurfer and ANTs tools. Basic statistics were employed to establish functional patterns and assess their statistical significance. Structural connectomes derived from probabilistic tractography on DWI images of 30 healthy subjects were employed.ResultResults revealed a significant preservation of the hippocampus in atypical AD phenotypes (p<0.05). The connectome analysis demonstrated variations in interconnections among late‐phase amyloid PET uptake regions, distinguishing between atypical and typical AD forms. Additionally, there is support for the hypothesis that early‐phase amyloid PET serves as a reliable marker for synaptic dysfunction.ConclusionIn conclusion, these preliminary findings suggest that structural connectomes in PET imaging can unveil unique neurodegenerative pathways in atypical AD patients. Moreover, the study supports the notion that early amyloid PET phases can provide insights into neurodegeneration among patients with atypical AD, akin to [18F]FDG PET.

Optimizing PET Intensity Normalization for the Analysis of Cerebral Metabolism: A Study in Simulated and Real Data from an Alzheimer’s Disease Model

Optimizing PET Intensity Normalization for the Analysis of Cerebral Metabolism: A Study in Simulated and Real Data from an Alzheimer’s Disease Model

Optimizing PET Intensity Normalization for the Analysis of Cerebral Metabolism: A Study in Simulated and Real Data from an Alzheimer's Disease Model

Optimizing PET Intensity Normalization for the Analysis of Cerebral Metabolism: A Study in Simulated and Real Data from an Alzheimer's Disease Model

Early Detection and Classification of Black Gram Plant Leaf Diseases Using ITL-CHB Method

The impact of black gram plant leaf diseases on global crop production is significant, resulting in considerable declines in the quantity and quality of agricultural commodities. Black gram is one of the most significant grain legumes in India, but its cultivation and production rate are heavily impacted by fungus attacks. To prevent the spread of black gram diseases, it is crucial to predict their presence in the premature stage through a computer-aided system. This paper proposes an automatic black gram plant disease (BPLD) detection system called “Improved Inception-V3 Transfer Learning based Crossover Honey Badger (ITL-CHB)” for disease detection and categorization. The BPLD dataset contains five different categories of plant disease images. In our BPLD dataset, several augmentation steps such as grayscale conversion, resizing, and intensity normalization are used to improve the data diversity and address the data imbalance issue. The pre-trained Inception-V3 model extracts features with well-defined hyperparameter values using the Crossover Honey Badger (CHB) algorithm. Thus, the proposed ITL-CHB approach predicts and classifies diseased and healthy leaf images with high accuracy. The effectiveness of the proposed model is examined using numerous performance metrics namely accuracy, sensitivity, precision, specificity, and f1-score. The comparative result demonstrates that the effectiveness of the ITL-CHB method achieved a higher accuracy rate of 98% compared to other existing techniques of SVM, Efficient Net, ResNet 50, and CNN in detecting and classifying black gram leaf diseases.

CTNI-67. AI BASED PREDICTION OF CLINICAL TTFIELDS RESPONSE IN GLIOBLASTOMA PATIENTS (RETROSPECTIVE ANALYSIS)

Abstract RESEARCH QUESTION Accurate and individualized prediction of response to therapies is central to precision medicine and a major goal in neuro-oncology. An extension of overall survival (OS) and progression-free survival (PFS) was observed in patients treated with tumor treating fields (TTFields) plus maintenance temozolomide compared to those treated with maintenance temozolomide alone and TTFields have become an integral part of the treatment of glioblastoma. To identify patients who benefit most from TTFields, we aim to implement a state-of-the-art machine learning approach to create a model capable of identifying responding patients early in treatment. METHODOLOGY In this retrospective analysis, patients with IDH-wildtype glioblastoma treated with TTFields, in addition to RT and temozolomide, as first-line therapy are included. Clinical data and MRI raw data (in DICOM format) are collected. The preprocessing of the MRI data includes reorientation, bias field correction, registration, segmentation, and intensity normalization. This is followed by the extraction of a Radiomics signature. The latter, along with the clinical data, is trained using various machine learning models. The model validation occurs on an independent test cohort. Data collection from various neuro-oncological centers across Europe is ongoing. RESULTS So far, data from 134 TTFields patients and 131 control patients have been collected. Initial results, providing detailed insights into the model architecture and performance, will be presented at the time of the SNO meeting. CONCLUSION This study has the potential to optimize the treatment of glioblastoma patients by using machine learning to allow the early identification of patients that respond to first line therapy.

Predicting major adverse cardiovascular events in suspected myocarditis patients using cmr late gadolinium enhancement radiomics: a time-to-event prognostication modeling

Abstract Background Myocarditis, characterized by inflammation of the heart tissue, exhibits a wide range of clinical manifestations; however, stratifying its risk is difficult. Objective We aimed to prognosticate Major Adverse Cardiovascular Events (MACE, i.e., death, sustained ventricular arrhythmia, heart failure hospitalization, and recurrent myocarditis) in patients with suspected myocarditis using cardiac magnetic resonate (CMR) late gadolinium enhancement (LGE) radiomic features. Method We enrolled consecutive 1099 patients (age: 47.68±16.56, 39.63% female) undergoing CMR with the initial suspicion of myocarditis based on the judgment of the referring physician, recruited from two major registries. We used fully automated deep learning tools to determine the segmental extent of LGE in the left ventricle. Furthermore, we pre-processed the dataset through bias-field correction and intensity normalization to ensure the model consistency across various image acquisitions. Different radiomic features, including shape, intensity, and texture, were extracted from CMR-LGE. We employed several feature selection methods and time-to-event survival models based on radiomics features. The development process, covering pre-processing, feature selection, and hyperparameter optimization, utilized 70% of the dataset (10-fold cross-validation). The evaluation was performed on the remaining 30% of the test set. Model performance was measured using the c-index and cumulative AUC (C-AUC) on the untouched 30% test set. Results The top-performing model, employing a 90% variance threshold with the survival support gradient boosting model, achieved a C-Index of 0.76 and a cumulative C-AUC of 0.80. The Random Forest Survival model was a close second, with a C-Index of 0.75 and a C-AUC of 0.79. The conventional multivariate Cox-PH model achieved a C-Index of 0.67 and a C-AUC of 0.70. Conclusion Utilizing radiomics features from the CMR-LGE, combined with machine learning algorithms, allows accurate prediction of MACE in suspected myocarditis patients. The performance of the Radiomics model could be improved by including other modality information.

Time varying flat field correction of X-ray microtomography with an improved deep-learning method.

In X-ray microtomography, the flat field image is usually needed to normalize the collected sample projections. Owing to the high brightness of the synchrotron radiation facility, dynamic CT imaging of in-situ or in-operando processes is broadly employed for the investigation of three-dimensional microstructure evolution. However, the fast, continuous data acquisition and the heavy, bulky in-situ devices usually prevent the easy collection of accurate flat field images, which means that conventional flat field correction is hard to efficiently correct the artefacts of X-ray microtomography. We report a deep-learning-based artefact correction method for X-ray microtomography, which uses flat field generated from each CT projection by an improved pix2pixHD model. Experimental results demonstrated that the proposed method has a significant advantage over the conventional method and available deep-learning-based flat field correction method for the flat field correction of projection images. The CT imaging results show that the proposed method efficiently reduces the systematic error during the intensity normalization process, and the CT reconstruction is improved significantly. Therefore, the method developed in this paper is applicable for the flat field correction of dynamic CT. Furthermore, experiments with a set of low Z material samples verified the generalization of the deep-learning-based method for a variety of samples never used for network training. In conclusion, the method developed in this paper is practicable for the flat field correction of in-situ CT imaging of dynamic processes and is also applicable to new samples as long as the neural network model is effectively trained.

18F]FDG Brain Imaging Assessment in Preclinical Acute and Chronic Stress Models Using Statistical Parametric Mapping

Previous studies have used [18F]FDG as a biomarker for depression, anxiety, or chronic stress in cancer patients1,2. However, they lack specific brain patterns and assessment in preclinical models, limiting their translational potential. Hence, we set out to establish preclinical [18F]FDG brain PET as a method to evaluate stress in vivo. Lean C57BL/6J mice were exposed to acute and chronic cooling, restraint, and surgical stress. Obese mice were on a high-fat diet for eight weeks and exposed to cooling and restraint stress. [18F]FDG was injected into fasted animals under brief isoflurane anesthesia via the tail vein. Following a 60-minute distribution phase during acute stress or one day after stress exposure (chronic), animals underwent static μPET/CT scans (Siemens® Inveon). Brain images were spatially normalized, and intensity normalization was performed using proportional and histogram-based scaling3 with PMOD (V. 3.8). Normalized images were analyzed using a brain atlas (M. Mirrione) to extract regional SUV and statistical parametric mapping (SPM12) for voxel-wise comparison. Overall, in lean mice, total brain [18F]FDG-uptake was significantly reduced across all stress models except for acute restraint, whereas total uptake was unaffected in obese mice. Subsequent brain atlas analysis of lean mice revealed changes in regional uptake for all stressors except for chronic cooling, while obese mice were affected by only acute and chronic restraint. Using SPM, we validated stress-specific hypo- and hypermetabolic areas in the brains of lean mice upon acute cooling, acute and chronic restraint, and surgery and in obese mice after chronic restraint (see Figure). We also identified overlapping areas in the brain stem and hypothalamus region following acute cooling and surgery, possibly due to post-surgical hypothermia. Our findings show that static [18F]FDG-imaging is a valuable tool for noninvasively assessing brain activity in a multitude of stress models and a suitable translational tool for stress research in mice. Please click on the 'PDF' for the full abstract!

Integrated framework for quantitative T2-weighted MRI analysis following prostate cancer radiotherapy

PurposeThe aim of this study is to develop a framework for quantitative analysis of longitudinal T2-weighted MRIs (T2w) following radiotherapy (RT) for prostate cancer. Materials and methodsThe developed methodology includes: (i) deformable image registration of longitudinal series to pre-RT T2w for automated detection of prostate, peripheral zone (PZ), and gross tumor volume (GTV); and (ii) T2w signal-intensity harmonization based on three reference tissues. The REgistration and HARMonization (REHARM) framework was applied on T2w acquired in a clinical trial consisting of two pre-RT and three post-RT MRI exams. Image registration was assessed by the DICE coefficient between automatic and manual contours, and intensity normalization via inter-patient histogram intersection. Longitudinal consistency was evaluated by the repeatability coefficient and Pearson correlation (r) between the two T2w exams before RT. ResultsT2w from 107 MRI exams (23 patients) were utilized. Following REHARM, the histogram intersections for prostate, PZ and GTV increased from median = 0.43/0.16/0.13 to 0.66/0.44/0.46. The repeatability in T2w intensity estimation was better for the automatic than the manual contours for all three regions of interest (r = 0.9, p < 0.0001, for GTV). The changes in the tissues’ T2w values pre- and post-RT became significant, indicating the measurable quantitative signal related to radiation. ConclusionsThe developed methodology allows to automate longitudinal analysis reducing data acquisition-related variation and improving consistency. The quantitative characterization of RT-induced changes in T2w will lead to new understanding of radiation effects enabling prediction modeling of RT response.

Radiomic detection of abnormal brain regions in tuberous sclerosis complex.

Radiomics refers to the extraction of quantitative information from medical images and is most commonly utilized in oncology to provide ancillary information for solid tumor diagnosis, prognosis, and treatment response. The traditional radiomic pipeline involves segmentation of volumes of interest with comparison to normal brain. In other neurologic disorders, such as epilepsy, lesion delineation may be difficult or impossible due to poor anatomic definition, small size, and multifocal or diffuse distribution. Tuberous sclerosis complex (TSC) is a rare genetic disease in which brain magnetic resonance imaging (MRI) demonstrates multifocal abnormalities with variable imaging and epileptogenic features. The purpose of this study was to develop a radiomic workflow for identification of abnormal brain regions in TSC, using a whole-brain atlas-based approach with generation of heatmaps based on signal deviation from normal controls. This was a retrospective pilot study utilizing high-resolution whole-brain 3D FLAIR MRI datasets from retrospective enrollment of tuberous sclerosis complex (TSC) patients and normal controls. Subjects underwent MRI including high-resolution 3D FLAIR sequences. Preprocessing included skull stripping, coregistration, and intensity normalization. Using the Brainnetome and Harvard-Oxford atlases, brain regions were parcellated into 318 discrete regions. Expert neuroradiologists spatially labeled all tubers in TSC patients using ITK-SNAP. The pyradiomics toolbox was used to extract 88 radiomic features based on IBSI guidelines, comparing tuber-affected and non-tuber-affected parenchyma in TSC patients, as well as normal brain tissue in control patients. For model training and validation, regions with tubers from 20 TSC patients and 30 normal control subjects were randomly divided into two training sets (80%) and two validation sets (20%). Additional model testing was performed on a separate group of 20 healthy controls. LASSO (least absolute shrinkage and selection operator) was used to perform variable selection and regularization to identify regions containing tubers. Relevant radiomic features selected by LASSO were combined to produce a radiomic score ω, defined as the sum of squared differences from average control group values. Region-specific ω scores were converted to heat maps and spatially coregistered with brain MRI to reflect overall radiomic deviation from normal. The proposed radiomic workflow allows for quantification of deviation from normal in 318 regions of the brain with the use of a summative radiomic score ω. This score can be used to generate spatially registered heatmaps to identify brain regions with radiomic abnormalities. The pilot study of TSC showed radiomic scores ω that were statistically different in regions containing tubers from regions without tubers/normal brain (p<0.0001). Our model exhibits an AUC of 0.81 (95% confidence interval: 0.78-0.84) on the testing set, and the best threshold obtained on the training set, when applied to the testing set, allows us to identify regions with tubers with a specificity of 0.91 and a sensitivity of 0.60. We describe a whole-brain atlas-based radiomic approach to identify abnormal brain regions in TSC patients. This approach may be helpful for identifying specific regions of interest based on relatively greater signal deviation, particularly in clinical scenarios with numerous or poorly defined anatomic lesions.

Determination of five-parameter grain boundary characteristics in nanocrystalline Ni-W by scanning precession electron diffraction tomography

Determining the full five-parameter grain boundary characteristics from experiments is essential for understanding grain boundaries impact on material properties, improving related models, and designing advanced alloys. However, achieving this is generally challenging, in particular at nanoscale, due to their 3D nature. In our study, we successfully determined the grain boundary characteristics of an annealed nickel-tungsten alloy (NiW) nanocrystalline needle-shaped specimen (tip) containing twins using Scanning Precession Electron Diffraction (SPED) Tomography. The presence of annealing twins in this face-centered cubic (fcc) material gives rise to common reflections in the SPED diffraction patterns, which challenges the reconstruction of orientation-specific virtual dark field (VDF) images required for tomographic reconstruction of the 3D grain shapes. To address this, an automated post-processing step identifies and deselects these shared reflections prior to the reconstruction of the VDF images. Combined with appropriate intensity normalization and projection alignment procedures, this approach enables high-fidelity 3D reconstruction of the individual grains contained in the needle-shaped sample volume. To probe the accuracy of the resulting boundary characteristics, the twin boundary surface normal directions were extracted from the 3D voxelated grain boundary map using a 3D Hough transform. For the sub-set of coherent Σ3 boundaries, the expected {111} grain boundary plane normals were obtained with an angular error of <3° for boundary sizes down to 400 nm². This work advances our ability to precisely characterize and understand the complex grain boundaries that govern material properties.

Developmental Curves of the Paediatric Brain Using FLAIR MRI Texture Biomarkers.

Purpose: Analysis of FLAIR MRI sequences is gaining momentum in brain maturation studies, and this study aimed to establish normative developmental curves for FLAIR texture biomarkers in the paediatric brain. Methods: A retrospective, single-centre dataset of 465/512 healthy paediatric FLAIR volumes was used, with one pathological volume for proof-of-concept. Participants were included if the MRI was unremarkable as determined by a neuroradiologist. An automated intensity normalization algorithm was used to standardize FLAIR signal intensity across MRI scanners and individuals. FLAIR texture biomarkers were extracted from grey matter (GM), white matter (WM), deep GM, and cortical GM regions. Sex-specific percentile curves were reported and modelled for each tissue type. Correlations between texture and established biomarkers including intensity volume were examined. Biomarkers from the pathological volume were extracted to demonstrate clinical utility of normative curves. Results: This study analyzed 465 FLAIR sequences in children and adolescents (mean age 10.65 ± 4.22 years, range 2-19 years, 220 males, 245 females). In the WM, texture increased to a maximum at around 8 to 10 years, with different trends between females and males in adolescence. In the GM, texture increased over the age range while demonstrating a local maximum at 8 to 10 years. Texture had an inverse relationship with intensity in the WM across all ages. WM and edema in a pathological brain exhibited abnormal texture values outside of the normative growth curves. Conclusion: Normative curves for texture biomarkers in FLAIR sequences may be used to assess brain maturation and microstructural changes over the paediatric age range.

Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models.

The current study explores the application of 3D U-Net architectures combined with Inception and ResNet modules for precise lung nodule detection through deep learning-based segmentation technique. This investigation is motivated by the objective of developing a Computer-Aided Diagnosis (CAD) system for effective diagnosis and prognostication of lung nodules in clinical settings. The proposed method trained four different 3D U-Net models on the retrospective dataset obtained from AIIMS Delhi. To augment the training dataset, affine transformations and intensity transforms were utilized. Preprocessing steps included CT scan voxel resampling, intensity normalization, and lung parenchyma segmentation. Model optimization utilized a hybrid loss function that combined Dice Loss and Focal Loss. The model performance of all four 3D U-Nets was evaluated patient-wise using dice coefficient and Jaccard coefficient, then averaged to obtain the average volumetric dice coefficient (DSCavg) and average Jaccard coefficient (IoUavg) on a test dataset comprising 53 CT scans. Additionally, an ensemble approach (Model-V) was utilized featuring 3D U-Net (Model-I), ResNet (Model-II), and Inception (Model-III) 3D U-Net architectures, combined with two distinct patch sizes for further investigation. The ensemble of models obtained the highest DSCavg of 0.84 ± 0.05 and IoUavg of 0.74 ± 0.06 on the test dataset, compared against individual models. It mitigated false positives, overestimations, and underestimations observed in individual U-Net models. Moreover, the ensemble of models reduced average false positives per scan in the test dataset (1.57 nodules/scan) compared to individual models (2.69-3.39 nodules/scan). The suggested ensemble approach presents a strong and effective strategy for automatically detecting and delineating lung nodules, potentially aiding CAD systems in clinical settings. This approach could assist radiologists in laborious and meticulous lung nodule detection tasks in CT scans, improving lung cancer diagnosis and treatment planning.

Optimizing anomaly detection in 3D MRI scans: The role of ConvLSTM in medical image analysis

The analysis of Medical Images (MI), particularly the detection and classification of anomalies in 3D MRI (Magnetic Resonance Imaging) scans, plays a critical part in timely intervention and personalized therapy plans. In our paper, a comprehensive methodology for anomaly detection in 3D MRI scans of the brain is proposed that combines advanced Deep Learning (DL) techniques, including Convolutional Long Short-Term Memory (ConvLSTM) model, with efficient statistical feature extraction from segmented anomaly regions. The data collection phase utilizes three main datasets namely the Brain Tumor Segmentation Challenge (BRATS), the Federated Tumor Segmentation Challenge (FETS), and the Medical Segmentation Decathlon (MSD). The research begins with preprocessing steps including image resizing, intensity normalization, and alignment of anomaly region segmentation masks. The targeted anomaly regions within the samples are segmented using U-Net architecture and then follow statistical feature extraction procedure. Dimensionality reduction methods such as Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) are utilized to streamline the feature space. The ConvLSTM is then used to classify the anomalies using both convolution and recurrent layers to capture spatiotemporal patterns in MRI data. The model is fine-tuned and iterated for better classification performance using Adam optimizer. The statistical evaluation results with accuracy of 98.9 % showed that the designed ConvLSTM method is more suitable for clinical diagnosis and treatment design in detecting anomalies in 3D MRI images.