Brain Image Segmentation Research Articles

Deep coupled registration and segmentation of multimodal whole-brain images.

Recent brain mapping efforts are producing large-scale whole-brain images using different imaging modalities. Accurate alignment and delineation of anatomical structures in these images are essential for numerous studies. These requirements are typically modeled as two distinct tasks: Registration and segmentation. However, prevailing methods, fail to fully explore and utilize the inherent correlation and complementarity between the two tasks. Furthermore, variations in brain anatomy, brightness and texture pose another formidable challenge in designing multi-modal similarity metrics. A high-throughput approach capable of overcomingthe bottleneck of multi-modal similarity metric design, while effective leveraging the highly correlated and complementary nature of two tasks is highly desirable. We introduce a deep learning framework for joint registration and segmentation of multi-modal brain images. Under this framework, registration and segmentation tasks are deeply coupled and collaborated at two hierarchical layers. In the inner layer, we establish a strong feature-level coupling between the two tasks by learning a unified common latent feature representation. In the outer layer, we introduce a mutually supervised dual-branch network to decouple latent features and facilitate task-level collaboration between registration and segmentation. Since the latent features we designed are also modality-independent, the bottleneck of designing multi-modal similarity metric is essentially addressed. Another merit offered by this framework is the interpretability of latent features, which allows intuitive manipulation of feature learning, thereby further enhancing network training efficiency and the performance of both tasks. Extensive experiments conducted on both multi-modal and mono-modal datasets of mouse and human brains demonstrate the superiority of our method. The code is available at https://github.com/tingtingup/DCRS. Supplementary data are available at Bioinformaticsonline.

Dementia diagnosis in young adults: a machine learning and optimization approach

AbstractIndividuals who are younger and have dementia often start experiencing its symptoms before they turn 65, with cases even documented in people as young as their thirties. Researchers strive for accurate dementia diagnosis to slow or halt its progression. This paper presents a novel Enhanced Dementia Detection and Classification Model (EDCM) comprised of four modules: data acquisition, preprocessing, hyperparameter optimization, and feature extraction/classification. Notably, the model uses texture information from segmented brain images for improved feature extraction, leading to significant gains in both binary and multi-class classification. This is achieved by selecting optimal features via a Gray Wolf Optimization (GWO)-driven enhancement model. Results demonstrate substantial accuracy improvements after optimization. For instance, using an Extra Tree Classifier for "normal" cases, the model achieves 85% accuracy before optimization. However, with GWO-optimized features and hyperparameters, the accuracy jumps to 97%.

Brain image segmentation with fuzzy entropy clustering and PSO-GWO optimization techniques

In the field of brain MRI analysis, image segmentation serves various purposes such as quantifying and visualizing anatomical structures, analyzing brain changes, delineating pathological regions, and aiding in surgical planning and image-guided interventions. Over the past few decades, diverse segmentation techniques with varying degrees of accuracy and complexity have been developed. Real-world brain MRI images often encounter intensity in homogeneity, posing a significant challenge in accurate segmentation. The prevailing image segmentation algorithms, predominantly region-based, typically rely on the homogeneity of image intensities in specific regions of interest. However, these methods often fall short of providing precise segmentation results due to intensity in homogeneity. To address these challenges and enhance segmentation performance, this paper introduce a novel objective function named Fuzzy Entropy Clustering with Local Spatial Information and Bias Correction (FECSB). Additionally, we propose a novel hybrid algorithm that combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to maximize the effectiveness of the FECSB function in MRI brain image segmentation. The proposed algorithm undergoes rigorous evaluation using benchmark MRI brain images, including those from the McConnell Brain Imaging Center (BrainWeb). The experimental results unequivocally demonstrate the superiority of the PSO-GWO clustering method over the traditional Fuzzy C Means (FCM) method. Across various image slices, the PSO-GWO method consistently outperforms FCM in terms of accuracy, showing improvements ranging from 1.28% to 1.46%, approximately achieving 99.37% accuracy.

Advanced Integration of Machine Learning Techniques for Accurate Segmentation and Detection of Alzheimer’s Disease

Alzheimer’s disease is a common type of neurodegenerative condition characterized by progressive neural deterioration. The anatomical changes associated with individuals affected by Alzheimer’s disease include the loss of tissue in various areas of the brain. Magnetic Resonance Imaging (MRI) is commonly used as a noninvasive tool to assess the neural structure of the brain for diagnosing Alzheimer’s disease. In this study, an integrated Improved Fuzzy C-means method with improved watershed segmentation was employed to segment the brain tissue components affected by this disease. These segmented features were fed into a hybrid technique for classification. Specifically, a hybrid Convolutional Neural Network–Long Short-Term Memory classifier with 14 layers was developed in this study. The evaluation results revealed that the proposed method achieved an accuracy of 98.13% in classifying segmented brain images according to different disease severities.

VINNA for Neonates – Orientation Independence through Latent Augmentations

Abstract A robust, fast, and accurate segmentation of neonatal brain images is highly desired to better understand and detect changes during development and disease, specifically considering the rise in imaging studies for this cohort. Yet, the limited availability of ground truth datasets, lack of standardized acquisition protocols, and wide variations of head positioning in the scanner pose challenges for method development. A few automated image analysis pipelines exist for newborn brain MRI segmentation, but they often rely on time-consuming non-linear spatial registration procedures and require resampling to a common resolution, subject to loss of information due to interpolation and down-sampling. Without registration and image resampling, variations with respect to head positions and voxel resolutions have to be addressed differently. In deep-learning, external augmentations such as rotation, translation, and scaling are traditionally used to artificially expand the representation of spatial variability, which subsequently increases both the training dataset size and robustness. However, these transformations in the image space still require resampling, reducing accuracy specifically in the context of label interpolation. We recently introduced the concept of resolution-independence with the Voxel-size Independent Neural Network framework, VINN. Here, we extend this concept by additionally shifting all rigid-transforms into the network architecture with a four degree of freedom (4-DOF) transform module, enabling resolution-aware internal augmentations (VINNA) for deep learning. In this work we show that VINNA (i) significantly outperforms state-of-the-art external augmentation approaches, (ii) effectively addresses the head variations present specifically in newborn datasets, and (iii) retains high segmentation accuracy across a range of resolutions (0.5-1.0mm). Furthermore, the 4-DOF transform module together with internal augmentations is a powerful, general approach to implement spatial augmentation without requiring image or label interpolation. The specific network application to newborns will be made publicly available as VINNA4neonates.

Brain MR image segmentation for tumor detection based on Riesz probability distributions

ABSTRACT This research introduces a new approach using the Riesz mixture model for medical image segmentation, specifically for diagnosing and treating brain tumors. We developed a novel technique for pixel classification based on the Riesz distribution, which is generated using an extended Bartlett decomposition. Our work is pioneering, as there are no existing studies addressing this issue in the literature. We aim to demonstrate the effectiveness of this distribution for brain image segmentation. We used the Expectation-Maximization algorithm to estimate the mixture parameters. To validate our segmentation algorithm, we conducted a comparative study with a recent method based on the Wishart distribution using Matlab software. Experiments with the Riesz mixture model showed that our method produces more intuitive results with a recognition rate of 94.52%. These results confirm the reliability of our method in detecting tumors using both synthetic and real brain images.

Cascade Residual Multiscale Convolution and Mamba-Structured UNet for Advanced Brain Tumor Image Segmentation

In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model’s potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning.

Mathematical Modeling and Statistical Exploration of Residual Computing Based Convolutional Neural Network Based Classifier for Complex Image Demarcation

The advent of deep learning has revolutionized the field of medical image analysis, particularly in complex tasks like brain image segmentation and classification. Among the various deep learning architectures, Convolutional Neural Networks (CNNs) have shown remarkable ability in extracting hierarchical features from medical images. The mathematical framework of our proposed Res-CNN is grounded in the principles of functional analysis and optimization theory. We employ the calculus of variations to model the propagation of activations within the network, optimizing a cost function tailored for the segmentation and classification of brain images. The structural integrity of the network is encoded through well-defined mathematical formulations, capturing the essence of residual blocks that allow the seamless flow of gradients during back propagation.from a statistical perspective, the robustness of the ResNet model is validated through rigorous exploratory data analysis. We systematically dissect the statistical properties of the network's predictions, employing methods such as hypothesis testing to ascertain the significance of the improvements offered by residual computing. Furthermore, Bayesian inference is utilized to gauge the uncertainty in the network's parameters, providing a probabilistic interpretation of the segmentation results. The efficacy of our model is further quantified through comprehensive experiments on diverse datasets of brain images. Statistical metrics such as accuracy, sensitivity, specificity, and area under the ROC curve (AUC) serve as the benchmarks for performance evaluation, solidifying the merit of our mathematical and statistical approach in the domain of medical image analysis. The ResNet50 demonstrates superior performance in segmenting and classifying interact patterns within brain images, paving the way for significant advancements in diagnostic methodologies.

Based on neural network cascade abnormal texture information dissemination of classification of patients with schizophrenia and depression

This study used MRI brain image segmentation to identify novel magnetic resonance imaging (MRI) biomarkers to distinguish patients with schizophrenia (SCZ), major depressive disorder (MD), and healthy control (HC). Brain texture measurements, including entropy and contrast, were calculated to capture variability in adjacent MRI voxel intensity. These measures are then applied to group classification in deep learning techniques and combined with hierarchical correlations to locate results. Texture feature maps were extracted from segmented brain MRI scans of 141 patients with schizophrenia (SCZ), 103 patients with major depressive disorder (MD) and 238 healthy controls (HC). Gray scale coassociation matrix (GLCM) is a monomer matrix calculated in a voxel cube. Deep learning methods were evaluated to determine the application capability of texture feature mapping in binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method is implemented by repeated nesting and cross-validation for feature selection. Regions that show the highest correlation (positive or negative). In this study, the authors successfully classified SCZ, MD and HC. This suggests that texture analysis can be used as an effective feature extraction method to distinguish different disease states. Compared with other methods, texture analysis can capture richer image information and improve classification accuracy in some cases. The classification accuracy of SCZ and HC, MD and HC, SCZ and MD reached 84.6%, 86.4% and 76.21%, respectively. Among them, SCZ and HC are the most significant features with high sensitivity and specificity.

Deep learning applications in MRI for brain tumor detection and image segmentation

Deep learning holds great potential in the field of MRI applications. By leveraging its advanced algorithms and neural networks, it can effectively analyze and interpret intricate patterns in medical images, aiding in precise disease detection, segmentation, and classification. Integrating deep learning techniques with MRI technology is expected to revolutionize radiology practice, facilitating enhanced diagnostic accuracy and customized treatment strategies, ultimately leading to improved patient outcomes. This article provides an overview of of the latest advancements in deep learning techniques applied to magnetic resonance imaging, specifically focusing on brain tumor detection and segmentation. The study examines eight different deep learning methods, including a multi-scale convolutional neural network, U-Net-based fully convolutional networks, cascaded anisotropic convolutional neural networks, missing modality-based tumor segmentation, Hough-CNN for deep brain region segmentation, k-Space deep learning for accelerated MRI, Multi-level Kronecker Convolutional Neural Network, and a heuristic approach for clinical brain tumor segmentation. Each method is analyzed, highlighting its specific techniques, advantages, and limitations. The comparative performance of these methods in terms of accuracy and efficiency, addressing key factors such as computational requirements, training time, and robustness, was discussed in this article. By assessing the merits and limitations of different approaches, this review seeks to offer valuable perspectives on effective utilization of deep learning techniques in clinical MRI settings for detecting and delineating brain tumors.

Neighbouring-slice Guided Multi-View Framework for brain image segmentation

Automated segmentation of brain images is a critical task in neuroscience for brain registration, atlas generation, etc. Deep learning techniques have been widely investigated for segmenting brain images, where most existing methods only consider each brain slice independently in a single view, limiting their ability to explore the correlation among adjacent slices and spatial brain structures. This paper proposes a Neighbouring-slice Guided Multi-view Framework for automated segmentation of brain images. To fully utilize the information between neighbouring slices, we design a dual-decoder network to segment targets (e.g., regions/tumors) in brain images and the edge of each brain targets simultaneously, by calculating the difference between adjacent slices. Considering the fact that some brain images cannot be fully recognized in a single view, we integrate the neighbouring-slice strategy in a multi-view segmentation framework to fully explore spatial structures in 3D brains. The proposed framework is validated on the automated segmentation of diverse brain tumors and brain regions including CTX (cerebellar cortex), CP (caudoputamen), HPF (hippocampal formation), BS (brain stem), CB (cerebellum), and CBX (cerebellar cortex), in LSFM (Light-sheet Fluorescence Microscopy) and MRI (Magnetic Resonance Imaging) modalities, demonstrating superior performance in comparison with state-of-the-arts. The codes are released at: https://github.com/NeuronXJTU/NGMV.

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.

Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation. This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation. A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods. Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

Unified Model for Children's Brain Image Segmentation with Co-Registration Framework Guided by Longitudinal MRI.

Accurate segmentation of brain structures is crucial for analyzing longitudinal changes in children's brains. However, existing methods are mostly based on models established at a single time-point due to difficulty in obtaining annotated data and dynamic variation of tissue intensity. The main problem with such approaches is that, when conducting longitudinal analysis, images from different time points are segmented by different models, leading to significant variation in estimating development trends. In this paper, we propose a novel unified model with co-registration framework to segment children's brain images covering neonates to preschoolers, which is formulated as two stages. First, to overcome the shortage of annotated data, we propose building gold-standard segmentation with co-registration framework guided by longitudinal data. Second, we construct a unified segmentation model tailored to brain images at 0-6 years old through the introduction of a convolutional network (named SE-VB-Net), which combines our previously proposed VB-Net with Squeeze-and-Excitation (SE) block. Moreover, different from existing methods that only require both T1- and T2-weighted MR images as inputs, our designed model also allows a single T1-weighted MR image as input. The proposed method is evaluated on the main dataset (320 longitudinal subjects with average 2 time-points) and two external datasets (10 cases with 6-month-old and 40 cases with 20-45 weeks, respectively). Results demonstrate that our proposed method achieves a high performance (>92%), even over a single time-point. This means that it is suitable for brain image analysis with large appearance variation, and largely broadens the application scenarios.

Brain image enhancement and segmentation using anatomically constrained neural networks

Brain image enhancement and segmentation using anatomically constrained neural networks

Improved Global U-Net applied for multi-modal brain tumor fuzzy segmentation

In this paper, we extended our work from Global U-Net combined with fuzzy amalgamation of Inception Model and Improved Kernel Variation for MRI Brain Image Segmentation [1] which was meant for single modality MRI images only to a brain tumor fuzzy segmentation. Many CNNs gives state of art results for a particular type of images. However, they cannot achieve the same result for the images captured from different imaging techniques. We experimented the Global U-Net model for MRI images earlier and this time we intended to make it applicable for other type of images too using the concept of fuzzy segmentation. The major concern was to overcome the limitations of single modality system that is not all the kernels of U-Net are capable of generating clear feature vectors for different image modalities. The result generated was satisfactory and we would further extend it for colored images.

Comparison of brain volume estimation via cerebral computed tomography and magnetic resonance imaging

AbstractBackgroundIn clinical routine, cerebral computed tomography (cCT) is easily accessible and more commonly used than brain magnetic resonance imaging (cMRI). However, to the best of our knowledge a fully automated, validated regional specific volume estimation tool is lacking. Therefore, the aim of this study was to investigate the association between tissue classification for gray matter of both modalities.MethodBrain imaging segmentations of cCT and structural MRI of 37 healthy controls and 20 patients with mild cognitive impairment due to Alzheimer’s disease were performed. The intensity and voxel count of normalized gray matter maps (MNI) in regions of interest using the Automated Anatomical Labelling Atlas (AAL3) were computed and compared between cCT and cMRI in both cohorts.ResultBrain Volume, measured by intensity and voxel count of gray matter, is associated between cortical regions acquired by cCT and cMRI. Although correlation coefficients varied between different brain areas, cortical compared to subcortical regions showed a relatively strong correlation.ConclusionThe evaluation of brain atrophy using segmented brain images acquired by cCT may be a simple, cost‐effective, and valuable addition to the clinical workflow, particularly in the context of socioeconomic and health disparities.

Comparison of brain volume estimation via cerebral computed tomography and magnetic resonance imaging

AbstractBackgroundIn clinical routine, cerebral computed tomography (cCT) is easily accessible and more commonly used than brain magnetic resonance imaging (cMRI). However, to the best of our knowledge a fully automated, validated regional specific volume estimation tool is lacking. Therefore, the aim of this study was to investigate the association between tissue classification for gray matter of both modalities.MethodBrain imaging segmentations of cCT and structural MRI of 37 healthy controls and 20 patients with mild cognitive impairment due to Alzheimer’s disease were performed. The intensity and voxel count of normalized gray matter maps (MNI) in regions of interest using the Automated Anatomical Labelling Atlas (AAL3) were computed and compared between cCT and cMRI in both cohorts.ResultBrain Volume, measured by intensity and voxel count of gray matter, is associated between cortical regions acquired by cCT and cMRI. Although correlation coefficients varied between different brain areas, cortical compared to subcortical regions showed a relatively strong correlation.ConclusionThe evaluation of brain atrophy using segmented brain images acquired by cCT may be a simple, cost‐effective, and valuable addition to the clinical workflow, particularly in the context of socioeconomic and health disparities.

Automated Brain Tumor Segmentation for MR Brain Images Using Artificial Bee Colony Combined With Interval Type-II Fuzzy Technique

Accurate prediction of brain tumors is vital while getting to the forum of medical image analysis, where precision in decision-making is of paramount importance, and the problems are to be addressed forthwith. For over a decade, innumerable medical imaging techniques using artificial intelligence and machine learning have been promulgated. The present research is intended to develop an algorithm that forges the working principles of the Artificial Bee Colony (ABC) and Interval Type-II fuzzy logic system (IT2FLS) algorithm to delineate the tumor region, which has been encompassed by complex brain tissues. The crux of any therapeutic sequences to be accomplished lies in the decisiveness of the oncologists, where the algorithm presented in this study significantly leverages decision-making through technological intervention. The algorithm proposed has versatility in handling a wide range of image sequences available in the BRATS challenge datasets (2015, 2017, and 2018) that have various levels of barriers, setbacks and hardships in identifying the aberrant regions, and it provides better segmentation outcomes that have been qualitatively validated and justified with metrics, such as DOI, specificity and sensitivity. Augmentation of the visual perception for oncologists is the insignia of this study, which in turn provides better insight and understanding regarding the ailment of the patient.

SVM for Classification of Brain MRI with K-Means for Detection

The segmentation of MR brain images using this method is based on K-means clustering, feature extraction using a discrete wavelet transform (DWT), and feature selection using a grey level co-occurrence matrix (GLCM). For this procedure, we have used a Perfect Radial Basis Function (RBF) - Support Vector Machine (SVM) Classifier. Based on fractions of selectivity and sensitivity, the classifier's performance was measured in terms of accuracy. The proposed classifier was found to be 93% accurate. Additionally, the Histogram methodology was applied in this proposed method in place of randomly choosing the cluster centres. Keywords: K-means clustering, Histon creation, Discrete Wavelet Transform (DWT), GLCM feature selection, and RBF- SVM Classifier.

Spiking neural networks fine-tuning for brain image segmentation.

The field of machine learning has undergone a significant transformation with the progress of deep artificial neural networks (ANNs) and the growing accessibility of annotated data. ANNs usually require substantial power and memory usage to achieve optimal performance. Spiking neural networks (SNNs) have recently emerged as a low-power alternative to ANNs due to their sparsity nature. Despite their energy efficiency, SNNs are generally more difficult to be trained than ANNs. In this study, we propose a novel three-stage SNN training scheme designed specifically for segmenting human hippocampi from magnetic resonance images. Our training pipeline starts with optimizing an ANN to its maximum capacity, then employs a quick ANN-SNN conversion to initialize the corresponding spiking network. This is followed by spike-based backpropagation to fine-tune the converted SNN. In order to understand the reason behind performance decline in the converted SNNs, we conduct a set of experiments to investigate the output scaling issue. Furthermore, we explore the impact of binary and ternary representations in SNN networks and conduct an empirical evaluation of their performance through image classification and segmentation tasks. By employing our hybrid training scheme, we observe significant advantages over both ANN-SNN conversion and direct SNN training solutions in terms of segmentation accuracy and training efficiency. Experimental results demonstrate the effectiveness of our model in achieving our design goals.