MRI Deep Learning-Based Solution for Alzheimer's Disease Prediction.

Skull striping is a crucial pre-processing step incorporated in several brain image processing applications. It deals with the removal of non-brain tissues from the brain Magnetic Resonance Imaging (MRI). The skull striping of brain MRI is not a trivial task due to the complex structure of the brain and presence of intensity in homogeneity artifact in MRI. In this paper a novel approach of skull stripping has been presented. The method is composed of adaptive iterative thresholding in addition to Otsu's global thresholding. The global thresholding is followed by analysis and removal of connected components. Finally morphological operations are carried out to obtain the brain mask. The method has been validated using 20 T1w normal coronal brain MRI images of Internet Brain Segmentation Repository (IBSR) dataset, 40 T1w MRI scans of LONI Probabilistic Brain Atlas project (LPBA40) dataset and 77 T1w MRI images from Open Access Series of Imaging Studies (OASIS) dataset. The comparative analysis using standard metrices (such as Dice Similarity Coefficient (DSC), Jacard Index (JI), sensitivity, and specificity) shows that the proposed method performs better than existing skull striping methods for brain MRI.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects older adults, leading to memory loss, cognitive decline, and behavioural changes.It is marked by the accumulation of amyloid-beta plaques and tau tangles in the brain, which impair neuronal function and eventually cause cell death.Diagnosis typically involves clinical evaluations, cognitive assessments, and imaging techniques such as MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) scans.Early detection is crucial, as it allows for interventions that can potentially slow the disease's progression, enabling patients and their families to manage symptoms more effectively.The transition from Normal Control (NC) to Early Mild Cognitive Impairment (EMCI) may be reversible, whereas other stages are not.This makes it a particularly challenging classification problem, as the changes in biomarker values are typically minimal during the progression from NC to very mild stages.To address this issue, we created a hybrid deep learning model, GDP (combining GoogLeNet, DenseNet-121, and Principal Component Analysis (PCA)), to extract hybrid features using 26639 sMRI images from well-known MRI datasets, such as the Open Access Series of Imaging Studies (OASIS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI).These features fed into the SVM binary classification algorithm to distinguish very mild cases from healthy images.We created synthetic images using CycleGAN augmentation to balance the dataset across labels and expand its size.The proposed GDP model stands out from current state-of-the-art deep learning techniques, such as ResNet-50, DenseNet-169, VGG-16, EfficientNet, and Inceptionv3, by achieving an impressive accuracy of 98.98% and an AUC of 99.91%.These results significantly surpass the performance of existing methods, making it a promising tool for early Alzheimer's disease prediction.

The medical diagnosis of diseases involves the detection and classification of patients from medical image data. It has an important Artificial Intelligence (AI) application in Healthcare and Neuroscience fields. In addition, Machine Learning (ML) and Deep Learning (DL) techniques provide important tasks on Neurodegenerative Diseases (ND) diagnosis from Brain MRI (Magnetic Resonance Imaging) data for image processing with good classification results. This chapter aims at comprehensive and comparative study on the performance of medical image classification with those techniques using Brain MRI datasets. We introduce a brief overview of the medical diagnosis of ND with AI applications in the neuroscience domain. In the literature part, we present the understanding of the medical image classification with Machine and Deep Learning techniques. Therefore, we describe the concept of MRI data and the Brain MRI datasets that are used in this work. In the comparison part, we focus on related works of the Artificial Neural Network (ANN) and the Deep Convolutional Neural Network (DCNN) models and we compare these models in the literature. In the methodology part, we combine the ANN and DCNN in order to propose a new Deep model named ANN-DCNN model when it is more accurate and effective to classify from those Brain images. To improve the performance of those Neural Networks in medical diagnosis applications, we discuss the recent insights and future directions.

Segmentation of brain magnetic resonance imaging (MRI) data plays an important role in the computer-aided diagnosis and neuroscience research. Fuzzy c-means (FCM) clustering algorithm is one of the most usually used techniques for brain MRI image segmentation because of its fuzzy nature. However, the conventional FCM method fails to carry out segmentation well enough due to intensity inhomogeneity in MRI data. To overcome this issue, we propose an improved algorithm based on FCM clustering for segmentation of brain MRI data. Specifically, we modify the conventional FCM algorithm to allow for intensity inhomogeneity by introducing the regularization of the neighborhood influence and bias field. Results show that our proposed algorithm obtains reasonable segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) from MRI data, which is superior to the expectation-maximization (EM) and conventional FCM methods.

e17105 Background: In patients suspected of prostate cancer, the decision to perform a biopsy hinges on available clinical data. Magnetic resonance imaging (MRI) and digital rectal exam (DRE) data are informative but may not be available. We created accurate models for clinically significant prostate cancer (csPCa), flexible to MRI and DRE data availability. Methods: Optimized ensembles of calibrated random forest models predicting csPCa (Grade Group ≥2) used total PSA, free PSA, prior negative biopsy status, and age, with or without DRE and MRI data (prostate volume and PI-RADS score). Risk models were derived (training cohorts n=1257 to 2191) and validated (validation cohorts n=317 to 1257) from different clinical sites. Models were evaluated by the area under the receiver operating characteristic curve (ROC AUC), sensitivity, specificity, positive predictive value, and negative predictive value, using thresholds providing ~ 95% sensitivity. Feature importance was determined by SHAP analysis. Results: All models had an AUC of at least 0.80, showing that predicting csPCa can be accurate without MRI or DRE data. Including MRI data significantly increased the AUC in the validation cohort (ClarityDX Prostate 0.80 vs ClarityDX Prostate +MRI 0.87). DRE had moderate value for models without MRI data (ClarityDX Prostate 0.80 vs ClarityDX Prostate +DRE 0.82) and minor value with MRI data (ClarityDX Prostate +MRI vs ClarityDX Prostate +DRE+MRI; AUC 0.87 vs 0.87; specificity 45% vs 47%, Table). Mean absolute SHAP values were highest for PI-RADS and prostate volume. Conclusions: These optimized risk models provide high accuracy for predicting csPCa in various clinical settings. Including MRI data greatly increases model accuracy, while DRE has a smaller effect on model accuracy. [Table: see text]

Multimodal neuroimaging has gained traction in Alzheimer's Disease (AD) diagnosis by integrating information from multiple imaging modalities to enhance classification accuracy. However, effectively handling heterogeneous data sources and overcoming the challenges posed by multiscale transform methods remains a significant hurdle. This article proposes a novel approach to address these challenges. To harness the power of diverse neuroimaging data, we employ a strategy that leverages optimized convolution techniques. These optimizations include varying kernel sizes and the incorporation of instance normalization, both of which play crucial roles in feature extraction from magnetic resonance imaging (MRI) and positron emission tomography (PET) images. Specifically, varying kernel sizes allow us to adapt the receptive field to different image characteristics, enhancing the model's ability to capture relevant information. Furthermore, we employ transposed convolution, which increases spatial resolution of feature maps, and it is optimized with varying kernel sizes and instance normalization. This heightened resolution facilitates the alignment and integration of data from disparate MRI and PET data. The use of larger kernels and strides in transposed convolution expands the receptive field, enabling the model to capture essential cross-modal relationships. Instance normalization, applied to each modality during the fusion process, mitigates potential biases stemming from differences in intensity, contrast, or scale between modalities. This enhancement contributes to improved model performance by reducing complexity and ensuring robust fusion. The performance of the proposed fusion method is assessed on three distinct neuroimaging datasets, which include: Alzheimer's Disease Neuroimaging Initiative (ADNI), consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, AD, and Early Mild Cognitive); Open Access Series of Imaging Studies (OASIS), consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, Mild Dementia, Very Mild Dementia); and whole-brain atlas neuroimaging (AANLIB) (consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, AD). To evaluate the quality of the fused images generated via our method, we employ a comprehensive set of evaluation metrics, including Structural Similarity Index Measurement (SSIM), which assesses the structural similarity between two images; Peak Signal-to-Noise Ratio (PSNR), which measures how closely the generated image resembles the ground truth; Entropy (E), which assesses the amount of information preserved or lost during fusion; the Feature Similarity Indexing Method (FSIM), which assesses the structural and feature similarities between two images; and Edge-Based Similarity (EBS), which measures the similarity of edges between the fused and ground truth images. The obtained fused image is further evaluated using a Mobile Vision Transformer. In the classification of AD vs. Cognitive Normal, the model achieved an accuracy of 99.00%, specificity of 99.00%, and sensitivity of 98.44% on the AANLIB dataset.

Alzheimer’s disease is a progressive neurodegenerative disorder requiring early and accurate detection for effective intervention. Deep learning (DL) techniques, particularly convolutional neural networks (CNNs), have shown promise in medical image classification. However, conventional CNN models often suffer from high computational complexity and inefficiency in handling imbalanced datasets. This study proposes a quad convolutional layers-CNN (QCL-CNN) for Alzheimer’s disease detection using magnetic resonance images (MRI) scans from the open access series of imaging studies (OASIS) dataset, which includes four dementia stages, non-dementia, very mild dementia, mild dementia, and moderate dementia. The QCL-CNN model employs four sequential convolutional layers for enhanced multi-level feature extraction, ensuring efficient classification while minimizing computational overhead. The experimental results demonstrate that QCL-CNN outperforms traditional CNN architectures, achieving an accuracy of 99.90%, recall of 99.89%, specificity of 99.93%, and an F1-score of 99.52%. The model surpasses VGG19, Xception, ResNet50, and DenseNet201 while maintaining a significantly lower parameter count (4.2M), making it computationally efficient. These findings confirm that network optimization is more crucial than model depth, ensuring robust performance even with fewer layers. Future research should explore multi-modal imaging, class balancing techniques, and real-world clinical validation to further improve the model’s diagnostic capabilities. The QCL-CNN model offers a promising artificial intelligence (AI)-powered approach for early Alzheimer’s detection, enabling precise, and efficient medical diagnosis.

Recent developments in deep learning techniques have gained significant attention in medical image analysis. Deep learning techniques have been shown to give promising results for automating various medical image tasks like segmentation of organs, precise delineation of lesions and automated disease diagnosis.We have demonstrated the utility of deep learning models for finding associations between brain imaging phenotypes and the molecular subtype. In this study Magnetic Resonance (MR) images of the brain with Glioblastoma multiforme (GBM) were used. The Cancer Genome Atlas (TCGA) has grouped GBM into four distinct subtypes, namely - Mesenchymal, Neural, Proneural and Classical. The subtype classification are defined by genomic characteristics, survival outcomes, patient age and response to treatment. Identification of molecular subtype and its associated imaging phenotype could aid in developing precision medicine and personalized treatments for patients.The MR imaging data and molecular subtype information were retrospectively obtained from The Cancer Imaging Archive (TCIA) of patients with high-grade gliomas. From the TCIA, 123 patient cases were manually identified which had the following four MR sequences- a) T1 and b) post-contrast T1-weighted (T1c), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (FLAIR). The MR dataset was further split into 92 and 31 cases for training and testing. The pre-processing of MR images involved skull-stripping, co-registration of MR sequences to T1c, re-sampling of MR volumes to isotropic voxels and segmentation of brain lesion. The lesions in the MR volumes were automatically segmented using a trained convolutional Neural Network (CNN) on BraTS2017 segmentation challenge dataset. From the segmentation maps 64×64×64 cube patches centered around the tumor were extracted from all the four MR sequences and a 3D convolutional neural network was trained for the molecular subtype classification. On the held-out test set, our approach achieved a classification accuracy of 90%. These results on TCIA dataset highlight the emerging role of deep learning in understanding molecular markers from non-invasive imaging phenotypes.

Background: This study evaluates the ability to predict the response of locally advanced breast cancers to neoadjuvant therapy (NAT) using patient-specific magnetic resonance imaging (MRI) data and a biophysical mathematical model. The 3D mathematical model consists of three parts: tumor cell proliferation, tumor spread (diffusion), and treatment. In particular, the tumor cells proliferate according to logistic growth, and the diffusion term is coupled to the mechanical properties of the surrounding fibroglandular and adipose tissues to inform individual tumor growth patterns (specific to each patient’s anatomy). The model’s treatment term accounts for tumor cell reduction according to approximate local drug delivery for each patient. Methods: Patients (N = 21) with intermediate to high grade invasive breast cancers with varying receptor status, who were eligible for NAT as a component of their clinical care, were recruited. Each patient was treated with standard-of-care consisting of one or two NAT regimens in sequence followed by surgical resection of any residual tumor. MRI data are acquired at four time points: 1) prior to initiation of NAT, 2) after 1 cycle of NAT, 3) after 2-4 cycles of NAT, and 4) 1 cycle after scan 3. The MRI data is processed and evaluated using our semi-automated pipeline. Specifically, diffusion-weighted MRI data is utilized to characterize the cellularity throughout the tumor tissue, and dynamic contrast-enhanced (DCE-) MRI data is used to segment the breast tissue and analyze the local drug delivery using pharmacokinetic analysis and population-derived plasma curves of drug concentrations. The model’s predictive ability is assessed using three different strategies. First, the model is calibrated using each patient’s first two scans to enable predictions of the total tumor cellularity, volume, and longest axis that are directly compared to the values measured from their third scan. Second, the model’s predictions for tumor response are compared to the corresponding response evaluation criteria in solid tumors (RECIST) results. Third, the model is re-calibrated using scans 3 and 4 and simulated to the time of surgery to compare the model’s predictions to each patient’s response status determined by surgical pathology. Results: Calibrating the model with MRI data for one cycle of therapy yields predictions strongly correlated with tumor response measured from each patient’s third scan, concordance correlation coefficients of 0.91, 0.90, and 0.86 for total cellularity, volume, and longest axis, respectively (p < 0.01, N = 18). The model’s predictions are significantly (p < 0.01) correlated with tumor response as designated by RECIST for the cohort. Specifically, the model predicts greater percent reduction in the longest axis for the RECIST designated responder group (i.e., complete response and partial response) compared to non-responders. At the time of surgery, the model predicts changes in total tumor cellularity from baseline that are significantly (p < 0.01) correlated with pathological response status—an area under the receiver operator characteristic curve of 0.92 and a sensitivity and specificity of 1.0 and 0.74, respectively. Discussion: These preliminary results suggest that this clinical-mathematical approach can be predictive of tumor response very early in the course of NAT on a patient-specific basis. Moreover, the study was performed in the community-care setting across a heterogenous group of patients, indicating the approach may be practical for wide-spread application. NCI U01 CA174706, NCI U01 CA154602, CPRIT RR160005, ACS-RSG-18-006-01-CCE, R01CA240589, NCI-U24CA226110, CPRIT RR160093 Citation Format: Angela M Jarrett, David A. Hormuth, II, Anum K Syed, Chengyue Wu, John Virostko, Anna G Sorace, Julie C DiCarlo, Jeanne Kowalski, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E Yankeelov. Predicting breast cancer response to neoadjuvant therapies using a mathematical model individualized with patient-specific magnetic resonance imaging data: Preliminary Results [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS13-18.

Biomarker assisted preclinical/early detection and intervention in Alzheimer’s disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

Unpaired multi-modal training and single-modal testing for detecting signs of endometriosis.

Categorizing brain cancers, including glioma, meningioma, and pituitary tumors, based on magnetic resonance imaging (MRI) images presents a significant challenge. Deep learning and machine learning techniques have shown promise in enhancing image categorization. To address this challenge, we leverage the power of the optimized ResNet50 model. Our approach involves classifying medical images using Convolutional Neural Network (CNN) features, which are then compared with the ResNet50 model. The primary goal is to detect brain tumors at an early stage using an advanced deep-learning model. We utilize an accessible dataset from Figshare, containing MRI images of the three distinct categories of brain tumors. Existing brain tumor models face limitations in handling multi-class problems and early-stage diagnosis. Therefore, we propose a fully automated approach employing Convolutional Neural Networks (CNN) to extract diverse properties from brain MRI scans. This method aims to provide accurate tumor diagnosis, even with a high number of classes and limited information in MRI data. Our proposed model involves the creation of identification blocks within a four-layered primary architecture, followed by testing and assessment of the interconnected layers. The results demonstrate that our model outperforms existing methods, achieving an impressive overall classification accuracy of 99.03%,

Objective: Automatic diagnosis of psychiatric disorders such as bipolar disorder (BD) through machine learning techniques has attracted substantial attention from psychiatric and artificial intelligence communities. These approaches mostly rely on various biomarkers extracted from electroencephalogram (EEG) or magnetic resonance imaging (MRI)/functional MRI (fMRI) data. In this paper, we provide an updated overview of existing machine learning-based methods for bipolar disorder (BD) diagnosis using MRI and EEG data. Method : This study is a short non-systematic review with the aim of describing the current situation in automatic diagnosis of BD using machine learning methods. Therefore, an appropriate literature search was conducted via relevant keywords for original EEG/MRI studies on distinguishing BD from other conditions, particularly from healthy peers, in PubMed, Web of Science, and Google Scholar databases. Results: We reviewed 26 studies, including 10 EEG studies and 16 MRI studies (including structural and functional MRI), that used traditional machine learning methods and deep learning algorithms to automatically detect BD. The reported accuracies for EEG studies is about 90%, while the reported accuracies for MRI studies remains below the minimum level for clinical relevance, i.e. about 80% of the classification outcome for traditional machine learning methods. However, deep learning techniques have generally achieved accuracies higher than 95%. Conclusion: Research utilizing machine learning applied to EEG signals and brain images has provided proof of concept for how this innovative technique can help psychiatrists distinguish BD patients from healthy people. However, the results have been somewhat contradictory and we must keep away from excessive optimistic interpretations of the findings. Much progress is still needed to reach the level of clinical practice in this field.

BackgroundBecause of superior soft tissue contrast, the use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To keep the workflow simple and cost effective and to reduce patient dose, it is natural to strive for a treatment planning procedure based entirely on MRI. In the present study, we investigate the dose calculation accuracy for different treatment regions when using bulk density assignments on MRI data and compare it to treatment planning that uses CT data.MethodsMR and CT data were collected retrospectively for 40 patients with prostate, lung, head and neck, or brain cancers. Comparisons were made between calculations on CT data with and without inhomogeneity corrections and on MRI or CT data with bulk density assignments. The bulk densities were assigned using manual segmentation of tissue, bone, lung, and air cavities.ResultsThe deviations between calculations on CT data with inhomogeneity correction and on bulk density assigned MR data were small. The maximum difference in the number of monitor units required to reach the prescribed dose was 1.6%. This result also includes effects of possible geometrical distortions.ConclusionsThe dose calculation accuracy at the investigated treatment sites is not significantly compromised when using MRI data when adequate bulk density assignments are made. With respect to treatment planning, MRI can replace CT in all steps of the treatment workflow, reducing the radiation exposure to the patient, removing any systematic registration errors that may occur when combining MR and CT, and decreasing time and cost for the extra CT investigation.

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