Search Results (184)

Background: Accurate in vivo brain tumor detection using hyperspectral imaging (HSI), a non-invasive technique that captures spectral information beyond the visible range, is challenging due to the complexity of biological tissues and the difficulty in distinguishing malignant from healthy areas. Conventional neural-network-based methods often misclassify tumor tissue as blood vessels, largely due to high vascularization and the scarcity of annotated data. Method: To address this issue, this work proposes an underexplored approach that decomposes the problem into two tasks: (1) segmentation of the brain cortical surface and its blood vessels, and (2) segmentation of biological tissues within the segmented craniotomy site. The cortical segmentation task is addressed independently of the segmentation model used in the second stage. To achieve this, a set of pseudo-labels is generated from RGB and HSI captures acquired during in vivo brain surgeries. These pseudo-labels support a multimodal training strategy that leverages both imaging domains, yielding a model capable of segmenting the craniotomy site and the blood vessels contained in it. The model is further refined on HSI using weakly supervised fine-tuning with sparse ground truth annotations. Results: The final segmentation map combines cortical and tissue segmentation outputs, considering only cortex pixels not overlapped by vessels as potential tumor regions. This simplifies the HSI tissue segmentation task, reframing it as a binary segmentation of healthy vs. other tissues, while still enabling a comprehensive multiclass output. Conclusions: The proposed method achieves up to a 15.48% increase in F1 score for the tumor class, while segmenting the brain cortex with a mean Dice similarity coefficient (DSC) of 92.08% and accurately detecting 95.42% of labeled blood vessel samples in the HSI dataset. Full article

Hyperspectral imaging (HSI) extends conventional histopathology by combining spatial morphology with rich spectral information that reflects tissue biochemical composition, offering new opportunities for quantitative tissue analysis. However, reliable spectral analysis requires accurate instance-level segmentation of cell nuclei to enable the construction of meaningful nuclear spectral databases. In this work, a comprehensive methodology for generating hyperspectral databases of cell nuclei from histopathological samples is presented, including hyperspectral acquisition, preprocessing, nucleus segmentation, and spectral signature extraction. Three nucleus segmentation methods are evaluated: a spectral-only approach based on pixel-wise hyperspectral signatures in the visible–VNIR range; a spatial-only approach using synthetic RGB images derived from hyperspectral cubes; and a combined spatial–spectral approach that jointly exploits spatial and spectral information. The methods are assessed on a proprietary dataset of 30 hyperspectral cubes of tumor and healthy histopathological brain tissue annotated by expert pathologists. The spectral-only method achieves a Dice similarity coefficient (DSC) of 61.89% and produces severe over-segmentation, with cell count deviations exceeding substantially the ground truth in healthy tissue. The spatial-only method attains the highest pixel-wise accuracy (78.97% DSC) but underestimates nucleus counts by approximately 30% in tumor regions due to nucleus merging. The spatial–spectral method achieves a DSC of 73.13% and a mean cell count deviation of 4%, providing more reliable instance-level separation. These findings demonstrate that pixel-wise accuracy alone is insufficient for hyperspectral nuclear database generation. Full article

Background: Deep brain stimulation (DBS) has traditionally followed diagnosis-driven, nucleus-centered targeting paradigms. Increasing evidence supports a circuit-based framework in which clinical outcomes depend on modulation of symptom-relevant networks rather than diagnostic labels alone. This approach is particularly relevant in mixed movement disorder phenotypes such as dystonic tremor, where the most disabling symptom may not align with the conventional surgical target. Methods: We report a clinically illustrative single case treated using a symptom-oriented, connectome-informed DBS strategy. Clinical phenotype, tremor severity, functional impairment, prior medical and botulinum toxin treatments, and longitudinal outcomes were systematically reviewed. DBS target selection prioritized the dominant, treatment-refractory symptom rather than the underlying dystonia diagnosis. Surgical planning incorporated high-resolution MRI with patient-specific thalamic segmentation using Brainlab Brain Elements^®, followed by postoperative lead localization and volume of tissue activated visualization with the SureTune™ platform. Results: A 54-year-old left-handed woman with long-standing cervical dystonia developed a severe, markedly asymmetric dystonic tremor predominantly affecting the left upper limb, resulting in profound functional disability. Instead of conventional bilateral globus pallidus internus DBS, unilateral right ventral intermediate nucleus (VIM) DBS was selected to engage tremor-related cerebellothalamic circuits. Rapid and marked improvement was observed, with tremor severity reduced to mild levels within 15 days after stimulation onset. At 6-month follow-up, overall tremor severity improved from 49 to 13 points on the Fahn–Tolosa–Marin Tremor Rating Scale, corresponding to a 73.5% reduction. This improvement was associated with restoration of legible handwriting, independent feeding and drinking, and recovery of bimanual fine motor function. Clinical benefit remained stable throughout follow-up, without stimulation-related adverse effects. Conclusions: This case illustrates the feasibility of a symptom-oriented, connectome-informed DBS strategy in selected patients with dystonic tremor. When symptom expression and network involvement are markedly asymmetric, selective unilateral modulation of the tremor-dominant circuit may achieve meaningful and durable functional improvement. Further studies are needed to assess the generalizability of this approach. Full article

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by a wide range of symptoms and severity, posing significant challenges for accurate diagnosis. Approaches that rely on a single data source, or unimodal data, often fail to capture the disorder’s inherent heterogeneity. A multimodal approach, which integrates diverse data types, can create a more holistic and precise understanding of ASD. This paper introduces the Multimodal ASD (MMASD) framework, a novel predictive model for ASD. The MMASD framework is built upon two distinct input modalities: structural magnetic resonance imaging (sMRI) and corresponding phenotype data. The sMRI data provides detailed neuroanatomical metrics, including brain tissue segmentation, volumetric measurements, and cortical thickness. Complementing this, the phenotype data encompasses the clinical and behavioral characteristics of each individual. In the proposed framework, latent features are independently extracted from both modalities and then fused to generate a comprehensive representation of the multimodal information. These fused features are then used to predict ASD by leveraging the outputs of various classifiers. A majority voting ensemble is employed to determine the final prediction. The MMASD framework achieves a high accuracy of 97.27%, surpassing the performance of current state-of-the-art approaches and demonstrating the efficacy of integrating neuroimaging and clinical data for ASD prediction. Full article

Background/Objectives: The earlier, more accurate, and more consistent prediction of the brain tumor recognition process requires automated systems to minimize diagnostic delays and human error. The automated system provides a platform for handling large medical images, speeding up clinical decision-making. However, the existing system is facing difficulties due to the high variability in tumor location, size, and shape, which leads to segmentation complexity. In addition, glioma-related tumors infiltrate the brain tissues, making it challenging to identify the exact tumor region. Method: The above-identified research difficulties are overcome by applying the Swin-UNet with cuttlefish-optimized attention-based Graph Neural Networks (SCAG-Net), thereby improving overall brain tumor recognition accuracy. This integrated approach is utilized to address infiltrative gliomas, tumor variability, and feature redundancy issues by improving diagnostic efficiency. Initially, the collected MRI images are processed using the Swin-UNet approach to identify the region, minimizing prediction error robustly. The region’s features are explored utilizing the cuttlefish algorithm, which minimizes redundant features and speeds up classification by improving accuracy. The selected features are further processed using the attention graph network, which handles structural and heterogeneous information across multiple layers, improving classification accuracy compared to existing methods. Results: The efficiency of the system, implemented with the help of public datasets such as BRATS 2018, BRATS 2019, BRATS 2020, and Figshare is ensured by the proposed SCAG-Net approach, which achieves maximum recognition accuracy. The proposed system achieved a Dice coefficient of 0.989, an Intersection over Union of 0.969, and a classification accuracy of 0.992. This performance surpassed the most recent benchmark models by margins of 1.0% to 1.8% and with statistically significant differences (p < 0.05). These findings present a statistically validated, computationally efficient, clinically deployable framework. Conclusions: The effective analysis of MRI complex structures is used in medical applications and clinical analysis. The proposed SCAG-Net framework significantly improves brain tumor recognition by addressing tumor heterogeneity and infiltrative gliomas using MRI images. The proposed approach provides a robust, efficient, and clinically deployable solution for brain tumor recognition from MRI images, supporting accurate and rapid diagnosis while maintaining expert-level performance. Full article

Presently, the segmentation of brain tumors from magnetic resonance imaging (MRI) scans is a very important challenge in the medical area, and it has a huge impact on correct diagnosis, efficient treatment planning, and patient prognosis. We present here the Contextual Transformer U-Net (CT-UNet), a novel deep learning approach that can significantly increase the accuracy and speed of brain tumor segmentation. The CT-UNet method features Transformer blocks embedded in a U-Net layout that extracts the most important contextual information across different types of MRI sequences, thereby drastically refining the delineation of tumor regions. We have tested CT-UNet on the Brain Tumor Segmentation (BraTS) challenge dataset that includes a large variety of tumor types, localization, and progression stages. To check the model’s performance, we used the Dice coefficient, sensitivity, specificity, precision, and Hausdorff distance metrics. The findings from our experiments demonstrate that CT-UNet has a substantial advantage over the classical segmentation model, and the 0.92 Dice coefficient it has achieved testifies to its state-of-the-art tumor localization in terms of both extent and form. Besides that, CT-UNet has achieved a very high sensitivity (0.90) and specificity (0.94); thus, it has been perfectly capable of discriminating tumor from non-tumor tissues. Spatial accuracy has also been improved significantly, as can be seen from the 7.5 mm Hausdorff distance achieved by this model, which means it can closely replicate the given tumor boundaries. By employing dynamic modality fusion and incorporating the Transformer mechanism into the established U-Net architecture, we have raised the bar for brain tumor segmentation. Our solution paves the way for another breakthrough in medical imaging technologies. CT-UNet not only speeds up the workflow of radiologists but also facilitates more targeted therapeutic strategies that may result in better patient care and prognosis. Yet the main goal of this work is to provide a basis for future studies that can consider incorporating deep learning methods in a routine clinical setting, thus paving the way for healthcare providers to benefit from both technical and clinical advantages. Full article

This study presents a zooarchaeological and taphonomic analysis of the previously unstudied component of the Mousterian faunal assemblage from Unit A9 at Grotta di Fumane (northeastern Italy), offering refined insights into Neanderthal subsistence behaviour during Marine Isotope Stage 3. Building on the previously published analysis of the principal portion of the assemblage, the new data reaffirm a subsistence strategy focused on selective transport and intensive on-site processing of high-utility carcass components. The ungulate assemblage—dominated by Cervus elaphus and Capreolus capreolus, with additional contributions from Rupicapra rupicapra and Capra ibex—is characterised by the dominance of hindlimb elements, moderate cranial representation, and a pronounced scarcity of axial remains. These patterns indicate that carcass reduction commenced at kill sites, where low-yield trunk segments were removed, while high-nutritional-value limb portions were preferentially transported to the cave for secondary processing. Taphonomic indicators, including abundant cut marks, percussion notches, and extensive bone fragmentation, demonstrate systematic defleshing, marrow extraction, and possible grease rendering within the cave, activities that were spatially associated with combustion features. Occasional cranial transport suggests targeted acquisition of high-fat tissues such as brains and tongue, behaviour consistent with cold-climate optimisation strategies documented in both ethnographic and experimental contexts. Collectively, the evidence indicates that Unit A9 served as a residential locus embedded within a logistically organised mobility system, where carcass processing, resource exploitation, and lithic activities were closely integrated. These findings reinforce the broader picture of late Neanderthals as adaptable and behaviourally sophisticated foragers capable of strategic planning and efficient exploitation of ungulate prey within the dynamic environments of northern Italy. Full article

Background/Objectives: The segmentation of three-dimensional radiological images constitutes a fundamental task in medical image processing for isolating tumors from complex datasets in computed tomography or magnetic resonance imaging. Precise visualization, volumetry, and treatment monitoring are enabled, which are critical for oncology diagnostics and planning. Volumetric analysis surpasses standard criteria by detecting subtle tumor changes, thereby aiding adaptive therapies. The objective of this study was to develop an enhanced, interactive Graphcut algorithm for 3D DICOM segmentation, specifically designed to improve boundary accuracy and 3D modeling of breast and brain tumors in datasets with heterogeneous tissue intensities. Methods: The standard Graphcut algorithm was augmented with a clustering mechanism (utilizing k = 2–5 clusters) to refine boundary detection in tissues with varying intensities. DICOM datasets were processed into 3D volumes using pixel spacing and slice thickness metadata. User-defined seeds were utilized for tumor and background initialization, constrained by bounding boxes. The method was implemented in Python 3.13 using the PyMaxflow library for graph optimization and pydicom for data transformation. Results: The proposed segmentation method outperformed standard thresholding and region growing techniques, demonstrating reduced noise sensitivity and improved boundary definition. An average Dice Similarity Coefficient (DSC) of 0.92 ± 0.07 was achieved for brain tumors and 0.90 ± 0.05 for breast tumors. These results were found to be comparable to state-of-the-art deep learning benchmarks (typically ranging from 0.84 to 0.95), achieved without the need for extensive pre-training. Boundary edge errors were reduced by a mean of 7.5% through the integration of clustering. Therapeutic changes were quantified accurately (e.g., a reduction from 22,106 mm³ to 14,270 mm³ post-treatment) with an average processing time of 12–15 s per stack. Conclusions: An efficient, precise 3D tumor segmentation tool suitable for diagnostics and planning is presented. This approach is demonstrated to be a robust, data-efficient alternative to deep learning, particularly advantageous in clinical settings where the large annotated datasets required for training neural networks are unavailable. Full article

Background: Hypoxic–ischemic (HI) brain injury triggers a dynamic, multi-phase response involving early microglial efferocytosis followed by extracellular matrix (ECM) deposition and scar formation. Arginase-1 (ARG1), a key enzyme in tissue repair, is implicated in both processes, yet its role in neonatal microglia remains poorly defined. We characterize ARG1-linked pathways in neonatal microglia, identifying distinct efferocytic and fibrotic phases post-HI. Methods: HI was induced in P9 mice using the Vannucci model, and brains were collected at 24 h (D1) and 5 days (D5). Spatially resolved single-cell transcriptomics (seqFISH) was performed using a targeted panel enriched for microglial, ARG1-pathway, efferocytosis, and profibrotic genes. Cell segmentation, clustering, and spatial mapping were conducted using Navigator and Seurat. Differential expression, GSEA, and enrichment analyses were used to identify time- and injury-dependent pathways. Results: Spatial transcriptomics identified 12 transcriptionally distinct cell populations with preserved neuroanatomical organization. HI caused the expansion of microglia and astrocytes and the loss of glutamatergic neurons by D5. Microglia rapidly activated regenerative and profibrotic programs—including TGF-β, PI3K–Akt, cytoskeletal remodeling, and migration—driven by early DEGs such as Cd44, Reln, TGF-β1, and Col1a2. By D5, microglia adopted a collagen-rich fibrotic state with an upregulation of Bgn, Col11a1, Anxa5, and Npy. Conclusion: Neonatal microglia transition from early efferocytic responses to later fibrotic remodeling after HI, driven by the persistent activation of PI3K–Akt, TGF-β, and Wnt/FZD4 pathways. These findings identify microglia as central regulators of neonatal scar formation and highlight therapeutic targets within ARG1-linked signaling. Full article

The aquaporin 0 (AQP0)/major intrinsic protein of eye lens (MIP) cDNA was cloned and sequenced. Initial studies of the tissue distribution of mRNA expression proved to be incorrect. Subsequent experiments showed that AQP0 mRNA is expressed strongly in the eye with moderately strong expression in the kidneys and some expression was seen in the brain and muscle tissue, and very low expression in the esophagus/fundic stomach. Another set of PCR reactions with five times the amount of cDNA additionally showed mRNA/cDNA expression in the liver, rectal gland, and a very low level in the intestine. Sporadic expression of different pieces of AQP0 cDNA was seen in various experiments in gill and pyloric stomach. A custom polyclonal antibody was produced against a region near the C-terminal end of the AQP0 protein sequence. The antibody gave a band of around the correct size (for the AQP0 protein) on the Western blot, which also showed a few other higher-molecular-weight bands. The antibody was also used in immunohistochemistry, and in the kidney, it showed staining in the proximal II (PII), intermediate segment I (IS I), and late distal tubule (LDT) parts of the sinus zone region of nephrons as well as some staining in the bundle zone tubule segments, suggesting a role for AQP0 as a water channel. In the rectal gland, the antibody showed weak apical membrane staining in a few secretory tubules near the duct, but also somewhat stronger staining in cells appearing to connect various secretory tubules, suggesting a role in cell–cell adhesion. In the spiral valve intestine side wall and valve flap, after signal amplification, weak antibody staining was seen in the apical and lateral membranes of epithelial cells adjacent to the luminal surface. There was also some staining in the intestinal muscle. In the rectum/colon, staining was seen in a layer of cells underlying the epithelium and in some muscle layers. In the gill, there was very weak staining in secondary lamellae epithelial cells and in connective tissue surrounding blood vessels and blood sinuses. The low level of transcript expression in the rectal gland, gill, and intestinal tissues suggests caution in the interpretation of the immunohistochemical staining in these tissues. Full article

In light of the growing prevalence of the autoimmune disease multiple sclerosis (MS), accurate detection of MS lesions in brain magnetic resonance imaging (MRI) images plays a critical role in assisting neurologists with timely diagnosis. The high similarity between MS lesions and normal brain tissues, however, makes this task particularly challenging. Although numerous deep-learning-based approaches have been proposed for the automatic segmentation of MS lesions, the method presented in this study has achieved superior results. ZechariahNet is a U-Net-based architecture that integrates transition down blocks, squeeze-attention (SA) blocks, dense blocks, and Convolutional LSTM (C-LSTM) blocks within a 3D CNN framework. By jointly exploiting spatial–temporal information from three consecutive MRI slices (previous, current, and subsequent) and strategically applying C-LSTM modules across the encoder and decoder paths, the proposed model effectively captures the neighborhood dependencies for enhanced feature extraction and reconstruction. These architectural innovations significantly improve segmentation accuracy, enabling ZechariahNet to achieve a dice similarity coefficient (DSC) of 84.72%, outperforming existing state-of-the-art methods. Full article

Magnetic resonance imaging (MRI) and hyperspectral imaging (HSI) provide complementary information for image-guided neurosurgery, combining high-resolution anatomical detail with tissue-specific optical characterization. This work presents a novel multimodal phantom dataset specifically designed for MRI–HSI integration. The phantoms reproduce a three-layer tissue structure comprising white matter, gray matter, tumor, and superficial blood vessels, using agar-based compositions that mimic MRI contrasts of the rat brain while providing consistent hyperspectral signatures. The dataset includes two designs of phantoms with MRI, HSI, RGB-D, and tracking acquisitions, along with pixel-wise labels and corresponding 3D models, comprising 13 phantoms in total. The dataset facilitates the evaluation of registration, segmentation, and classification algorithms, as well as depth estimation, multimodal fusion, and tracking-to-camera calibration procedures. By providing reproducible, labeled multimodal data, these phantoms reduce the need for animal experiments in preclinical imaging research and serve as a versatile benchmark for MRI–HSI integration and other multimodal imaging studies. Full article

Background: Common variable immunodeficiency is a heterogeneous disorder characterized by defects in antibody production and immune dysregulation, associated with infections and autoimmunity. Although structural and cognitive effects of CVID on the central nervous system have attracted attention in recent years, studies jointly addressing volumetric brain imaging and neurocognitive evaluation remain limited. Materials and Methods: In this retrospective cross-sectional study, 35 patients with common variable immunodeficiency and 40 age- and sex-matched healthy controls were evaluated. Cognitive performance was assessed in all participants using the Montreal Cognitive Assessment. High-resolution T1-weighted brain magnetic resonance imaging scans underwent automated segmentation using the volBrain platform, yielding quantitative volumetric measurements of cortical, subcortical, and cerebellar structures, as well as ventricles and cerebrospinal fluid. Intergroup comparisons were performed using independent t-tests and analysis of variance. Results: MoCA scores were significantly lower in patients with CVID. Volumetric analysis revealed prominent reductions in the volumes of total brain tissue, gray matter, cerebrum, cerebellum, limbic system, thalamus, and basal ganglia. Paralleling these findings, cerebrospinal fluid and lateral ventricle volumes were increased. Additional volume losses were detected in CVID patients with low MoCA scores. In CVID patients with autoimmunity, volume loss affected broader areas. Conclusions: CVID appears to be associated with structural brain changes and cognitive impairments. Chronic inflammation and immune dysregulation may contribute to these neurodegenerative processes. Regular neurocognitive monitoring and further prospective studies are warranted in patients with CVID. Full article

Background: Brain tumors present a significant clinical problem due to high mortality and strong heterogeneity in size, shape, location, and tissue characteristics, complicating reliable MRI analysis. Existing automated methods are limited by non-selective skip connections that propagate noise, axis-separable attention modules that poorly integrate channel and spatial cues, shallow encoders with insufficiently discriminative features, and isolated optimization of segmentation or classification tasks. Methods: We propose a model using an EfficientNetV2S backbone with a Hierarchical Hybrid Attention (HHA) mechanism. The HHA couples a global-context pathway with a local-spatial pathway, employing a correlation-driven, per-pixel fusion gate to explicitly model interactions between them. Multi-scale dilated blocks are incorporated to enlarge the effective receptive field. The model is applied to a multiclass brain tumor MRI dataset, leveraging shared representation learning for joint segmentation and classification. Results: The design attains a Dice score of 92.25% and a Jaccard index of 86% for segmentation. For classification, it achieves an accuracy of 99.53%, with precision, recall, and F1 scores all close to 99%. These results indicate sharper tumor boundaries, stronger noise suppression in segmentation, and more robust discrimination in classification. Conclusions: The proposed framework effectively overcomes key limitations in brain tumor MRI analysis. The integrated HHA mechanism and shared representation learning yield superior segmentation quality with enhanced boundary delineation and noise suppression, alongside highly accurate tumor classification, demonstrating strong clinical utility. Full article

Background/Objectives: Glioblastoma (GBM) is an aggressive primary brain tumor marked by diffuse infiltration into surrounding brain tissue. The peritumoral zone often appears normal on imaging yet harbors microscopic invasion. While perfusion-based studies, such as arterial spin labeling (ASL), have profiled this region, longitudinal radiomic monitoring remains limited. This study investigates delta radiomics using multiparametric MRI (mpMRI) in a GBM mouse model to track subtle peritumoral changes over time. Methods: A G7 GBM xenograft model was established in nine nude mice, imaged at 9- and 12 weeks post-implantation using MRI (T1W, T2W, T2 mapping, DWI-ADC, FA, and ASL) and co-registered histopathology (H&E, HLA staining). Tumor and peritumoral regions were manually segmented, and 107 radiomic features (shape, first-order, texture) were extracted per sequence and histology. The delta features were calculated and compared between timepoints. Results: The robust T2W texture and T2 map first-order features demonstrated the greatest sensitivity and reproducibility in capturing temporal peritumoral brain zone changes, distinguishing between time points used by K-mean. Conclusions: Delta radiomics offers added value over static analysis for early monitoring of peritumoral brain zone changes. The first-order and texture features of radiomics could serve as robust biomarkers of peritumoral invasion. These findings highlight the potential of longitudinal MRI-based radiomics to characterize glioblastoma progression and inform translational research. Full article