The primary goal in neuro-oncology is the maximally safe resection of high-grade glioma (HGG). A more extensive resection improves both overall and disease-free survival, while a complication-free surgery enables better tolerance to adjuvant therapies such as chemotherapy and radiotherapy. Techniques such as 5-aminolevulinic acid (5-ALA) fluorescence and intraoperative ultrasound (ioUS) are valuable for safe resection and cost-effective. However, the benefits of combining these techniques remain undocumented. The aim of this study was to investigate outcomes when combining 5-ALA and ioUS. From January 2019 to January 2024, 72 patients (mean age 62.2 years, 62.5% male) underwent HGG resection at a single hospital. Tumor histology included glioblastoma (90.3%), grade IV astrocytoma (4.1%), grade III astrocytoma (2.8%), and grade III oligodendroglioma (2.8%). Tumor resection was performed under natural light, followed by using 5-ALA and ioUS to detect residual tumor. Biopsies from the surgical bed were analyzed for tumor presence and categorized based on 5-ALA and ioUS results. Results of 5-ALA and ioUS were classified into positive, weak/doubtful, or negative. Histological findings of the biopsies were categorized into solid tumor, infiltration, or no tumor. Sensitivity, specificity, and predictive values for both techniques, separately and combined, were calculated. A machine learning algorithm (HGGPredictor) was developed to predict tumor presence in biopsies. The overall sensitivities of 5-ALA and ioUS were 84.9% and 76%, with specificities of 57.8% and 84.5%, respectively. The combination of both methods in a positive/positive scenario yielded the highest performance, achieving a sensitivity of 91% and specificity of 86%. The positive/doubtful combination followed, with sensitivity of 67.9% and specificity of 95.2%. Area under the curve analysis indicated superior performance when both techniques were combined, in comparison to each method used individually. Additionally, the HGGPredictor tool effectively estimated the quantity of tumor cells in surgical margins. Combining 5-ALA and ioUS enhanced diagnostic accuracy for HGG resection, suggesting a new surgical standard. An intraoperative predictive algorithm could further automate decision-making.

Intracranial aneurysms (IAs) are abnormal dilations of cerebral blood vessels that, if ruptured, can lead to life-threatening consequences. However, their small size and soft contrast in radiological scans often make it difficult to perform accurate and efficient detection and morphological analyses, which are critical in the clinical care of the disorder. Furthermore, the lack of large public datasets with voxel-wise expert annotations pose challenges for developing deep learning algorithms to address the issues. Therefore, we proposed a novel weakly supervised 3D multi-task UNet that integrates vesselness priors to jointly perform aneurysm detection and segmentation in time-of-flight MR angiography (TOF-MRA). Specifically, to robustly guide IA detection and segmentation, we employ the popular Frangi's vesselness filter to derive soft cerebrovascular priors for both network input and an attention block to conduct segmentation from the decoder and detection from an auxiliary branch. We train our model on the Lausanne dataset with coarse ground truth segmentation, and evaluate it on the test set with refined labels from the same database. To further assess our model's generalizability, we also validate it externally on the ADAM dataset. Our results demonstrate the superior performance of the proposed technique over the SOTA techniques for aneurysm segmentation (Dice = 0.614, 95%HD =1.38mm) and detection (false positive rate = 1.47, sensitivity = 92.9%).

Accurate identification and segmentation of brain tumors in Magnetic Resonance Imaging (MRI) images are critical for timely diagnosis and treatment. MRI is frequently used to diagnose these disorders; however medical professionals find it challenging to manually evaluate MRI pictures because of time restrictions and unpredictability. Computerized methods such as R-CNN, attention models and earlier YOLO variants face limitations due to high computational demands and suboptimal segmentation performance. To overcome these limitations, this study proposes a successive framework that evaluates YOLOv9, YOLOv10, and YOLOv11 for tumor detection and segmentation using the Figshare Brain Tumor dataset (2100 images) and BraTS2020 dataset (3170 MRI slices). Preprocessing involves log transformation for intensity normalization, histogram equalization for contrast enhancement, and edge-based ROI extraction. The models were trained on 80% of the combined dataset and evaluated on the remaining 20%. YOLOv11 demonstrated superior performance, achieving 96.22% classification accuracy on BraTS2020 and 96.41% on Figshare, with an F1-score of 0.990, recall of 0.984, [email protected] of 0.993, and mAP@ [0.5:0.95] of 0.801 during testing. With a fast inference time of 5.3 ms and a balanced precision-recall profile, YOLOv11 proves to be a robust, real-time solution for brain tumor detection in clinical applications.

Hyperspectral imaging (HSI) provides rich spectral information for medical imaging, yet encounters significant challenges due to data limitations and hardware variations. We introduce SAMSA, a novel interactive segmentation framework that combines an RGB foundation model with spectral analysis. SAMSA efficiently utilizes user clicks to guide both RGB segmentation and spectral similarity computations. The method addresses key limitations in HSI segmentation through a unique spectral feature fusion strategy that operates independently of spectral band count and resolution. Performance evaluation on publicly available datasets has shown 81.0% 1-click and 93.4% 5-click DICE on a neurosurgical and 81.1% 1-click and 89.2% 5-click DICE on an intraoperative porcine hyperspectral dataset. Experimental results demonstrate SAMSA's effectiveness in few-shot and zero-shot learning scenarios and using minimal training examples. Our approach enables seamless integration of datasets with different spectral characteristics, providing a flexible framework for hyperspectral medical image analysis.

Early diagnosis of Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI), is critical for effective prevention and treatment. Computer-aided diagnosis using magnetic resonance imaging (MRI) provides a cost-effective and objective approach. However, existing methods often segment 3D MRI images into 2D slices, leading to spatial information loss and reduced diagnostic accuracy. To overcome this limitation, we propose TA-SSM Net, a deep learning model that leverages tri-directional attention and structured state-space model (SSM) for improved MRI-based diagnosis of AD and MCI. The tri-directional attention mechanism captures spatial and contextual information from forward, backward, and vertical directions in 3D MRI images, enabling effective feature fusion. Additionally, gradient checkpointing is applied within the SSM to enhance processing efficiency, allowing the model to handle whole-brain scans while preserving spatial correlations. To evaluate our method, we construct a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), consisting of 300 AD patients, 400 MCI patients, and 400 normal controls. TA-SSM Net achieved an accuracy of 90.24% for MCI detection and 95.83% for AD detection. The results demonstrate that our approach not only improves classification accuracy but also enhances processing efficiency and maintains spatial correlations, offering a promising solution for the diagnosis of Alzheimer's disease.

This study aimed to develop and assess an advanced Attention-Based Residual U-Net (ResUNet) model for accurately segmenting different types of brain hemorrhages from CT images. The goal was to overcome the limitations of manual segmentation and current automated methods regarding precision and generalizability. A dataset of 1,347 patient CT scans was collected retrospectively, covering six types of hemorrhages: subarachnoid hemorrhage (SAH, 231 cases), subdural hematoma (SDH, 198 cases), epidural hematoma (EDH, 236 cases), cerebral contusion (CC, 230 cases), intraventricular hemorrhage (IVH, 188 cases), and intracerebral hemorrhage (ICH, 264 cases). The dataset was divided into 80% for training using a 10-fold cross-validation approach and 20% for testing. All CT scans were standardized to a common anatomical space, and intensity normalization was applied for uniformity. The ResUNet model included attention mechanisms to enhance focus on important features and residual connections to support stable learning and efficient gradient flow. Model performance was assessed using the Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and directed Hausdorff distance (dHD). The ResUNet model showed excellent performance during both training and testing. On training data, the model achieved DSC scores of 95 ± 1.2 for SAH, 94 ± 1.4 for SDH, 93 ± 1.5 for EDH, 91 ± 1.4 for CC, 89 ± 1.6 for IVH, and 93 ± 2.4 for ICH. IoU values ranged from 88 to 93, with dHD between 2.1- and 2.7-mm. Testing results confirmed strong generalization, with DSC scores of 93 for SAH, 93 for SDH, 92 for EDH, 90 for CC, 88 for IVH, and 92 for ICH. IoU values were also high, indicating precise segmentation and minimal boundary errors. The ResUNet model outperformed standard U-Net variants, achieving higher multi-label segmentation accuracy. This makes it a valuable tool for clinical applications that require fast and reliable brain hemorrhage analysis. Future research could investigate semi-supervised techniques and 3D segmentation to further enhance clinical use. Not applicable.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder and the early diagnosis is crucial for managing symptoms and slowing disease progression. This paper proposes a framework named Federated Learning Enabled Waterwheel Shuffled Shepherd Optimization-based Efficient-Fuzzy Deep Maxout Network (FedL_WSSO based Eff-FDMNet) for PD detection and classification. In local training model, the input image from the database "Image and Data Archive (IDA)" is given for preprocessing that is performed using Gaussian filter. Consequently, image augmentation takes place and feature extraction is conducted. These processes are executed for every input image. Therefore, the collected outputs of images are used for PD detection using Shepard Convolutional Neural Network Fuzzy Zeiler and Fergus Net (ShCNN-Fuzzy-ZFNet). Then, PD classification is accomplished using Eff-FDMNet, which is trained using WSSO. At last, based on CAViaR, local updation and aggregation are changed in server. The developed method obtained highest accuracy as 0.927, mean average precision as 0.905, lowest false positive rate (FPR) as 0.082, loss as 0.073, Mean Squared Error (MSE) as 0.213, and Root Mean Squared Error (RMSE) as 0.461. The high accuracy and low error rates indicate that the potent framework can enhance patient outcomes by enabling more reliable and personalized diagnosis.

We developed new deep learning-based hierarchical brain segmentation (DLHBS) method that can segment T1-weighted MR images (T1WI) into 107 brain subregions and calculate the volume of each subregion. This study aimed to evaluate the repeatability and reproducibility of volume estimation using DLHBS and compare them with those of representative brain segmentation tools such as statistical parametric mapping (SPM) and FreeSurfer (FS). Hierarchical segmentation using multiple deep learning models was employed to segment brain subregions within a clinically feasible processing time. The T1WI and brain mask pairs in 486 subjects were used as training data for training of the deep learning segmentation models. Training data were generated using a multi-atlas registration-based method. The high quality of training data was confirmed through visual evaluation and manual correction by neuroradiologists. The brain 3D-T1WI scan-rescan data of the 11 healthy subjects were obtained using three MRI scanners for evaluating the repeatability and reproducibility. The volumes of the eight ROIs-including gray matter, white matter, cerebrospinal fluid, hippocampus, orbital gyrus, cerebellum posterior lobe, putamen, and thalamus-obtained using DLHBS, SPM 12 with default settings, and FS with the "recon-all" pipeline. These volumes were then used for evaluation of repeatability and reproducibility. In the volume measurements, the bilateral thalamus showed higher repeatability with DLHBS compared with SPM. Furthermore, DLHBS demonstrated higher repeatability than FS in across all eight ROIs. Additionally, higher reproducibility was observed with DLHBS in both hemispheres of six ROIs when compared with SPM and in five ROIs compared with FS. The lower repeatability and reproducibility in DLHBS were not observed in any comparisons. Our results showed that the best performance in both repeatability and reproducibility was found in DLHBS compared with SPM and FS.

This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Resonance Imaging (MRI) images. Using Fluid-Attenuated Inversion Recovery (FLAIR) abnormality segmentation masks and MRI scans from The Cancer Genome Atlas's (TCGA's) lower-grade glioma collection, our proposed approach uses a VGG19-based U-Net architecture with fixed pretrained weights. The experimental findings, which show an Area Under the Curve (AUC) of 0.9957, F1-Score of 0.9679, Dice Coefficient of 0.9679, Precision of 0.9541, Recall of 0.9821, and Intersection-over-Union (IoU) of 0.9378, show how effective the proposed framework is. According to these metrics, the VGG19-powered U-Net outperforms not only the conventional U-Net model but also other variants that were compared and used different pre-trained backbones in the U-Net encoder.Clinical trial registrationNot applicable as this study utilized existing publicly available dataset and did not involve a clinical trial.

Cognitive impairment (CI) in people with MS (pwMS) has complex pathophysiology. Neuropsychological testing (NPT) can be helpful, but interpretation may be challenging for clinicians. Thalamic atrophy (TA) has shown correlation for both neurodegeneration and CI. Leverage machine learning methods to link CI and longitudinal neuroimaging biomarkers. Retrospective review of adult pwMS with NPT and ≥2 brain MRIs. Quantitative MRI regional change rates were calculated using mixed effects models. Participants were divided into training and validation cohorts. K-means clustering was done based on first and second NPT principal components (PC1 and PC2). MRI change rates were compared between clusters. 112 participants were included (mean age 48 years, 71 % female, 80 % relapsing remitting). Processing speed and memory were the major contributors to PC1. We identified two clusters based on PC1, one with significantly more TA in both training and validation cohorts (p = 0.035; p = 0.002) and similar rates of change in all other quantitative MRI measures. The most important contributors to PC1 included measures of processing speed (SDMT/WAIS Coding) and memory (List Learning/BVMT immediate and delayed recall). This clustering method identified a profile of NPT results strongly linked to and possibly driven by TA. These results confirm validity of previously established findings using more advanced analyses in addition to offering novel insights into NPT dimensionality reduction.