Mutated Aquila Optimizer for assisting brain tumor segmentation

Brain tumor segmentation from MRI is an important task in biomedical image processing that can help specialists to predict diseases and to improve their diagnoses. Nowadays, most of the state-of-the-art techniques are based on deep learning neural networks for which the choice of the best architecture remains an open question. Hence, this paper aims at providing answers through an intensive and comprehensive comparison between several promising neural network architectures. Our study leads us to three approaches which are respectively based on 2D U-Net, 3D U-Net and cascaded neural networks, that are compared together and with another unsupervised technique based on k-mean clustering. We also consider several enhancement techniques such as data augmentation, curriculum learning and an original boosting method based on majority voting. We achieve to improve the results of the baseline methods in terms of Dice score when the suitable combination of techniques is used.

A novel automatic image segmentation technique in magnetic resonance imaging (MRI) based on di‐phase midway convolution and deconvolution network is proposed. It consists of three convolutional and deconvolutional blocks for downsampling and upsampling layers respectively. In first block, each input slice is separately convolved using two paths with 3 × 3 and 7 × 7 kernels to produce different feature maps. Then the mean value of these feature maps is processed into upcoming blocks in downsampling and upsampling layers. This processed outcome is classified and segmented using softmax classification. Further, the volume, probability density distribution of tumor, and normal tissue regions are calculated using tissue‐type mapping technique. This method is extensively tested with BRATS 2012, BRATS 2013, and BRATS 2018 data sets. Our experimental results achieved higher dice similarity coefficient values of 24.3%, 27.5%, and 3.4%, respectively, for these three data sets when compared to the state‐of‐art brain tumor segmentation methods.

Segmentation of brain tumor from magnetic resonance imaging is a challenging and time-consuming task due to the unpredictable appearance of tumor tissue in practical applications. In this paper we propose a novel level-set-based model for tumor segmentation from multi-modality magnetic resonance imaging. We formulate the tumor segmentation on pixel-level with three classes: tumor, edema and healthy tissue. First, we detect abnormal regions by using a region-based active contour model on T2-weighted images with fluid-attenuated inversion recovery modality, and a variational level set formulation is applied locally to approximate the image intensities on two sides of the contour. In the second stage, we distinguish the edema and tumor tissues in the abnormal regions based on the contrast enhancement T1 modality. Compared with traditional one-modality methods, our model can better represent the specific tissue of tumor in a simple way. The validation experiments on synthetic and clinical brain magnetic resonance images demonstrate the effectiveness and simplicity of the proposed method for brain tumor segmentation.

Brain tumor segmentation and classification is really a difficult process to identify and detect the tumor region. Magnetic resonance image (MRI) gives valuable information to find the affected area in the brain. The MRI brain image is initially considered, which specifies four various modalities of the brain such as T1, T2, T1C, and the Flair. The preprocessing methodologies and the state-of-the-art MRI-related brain tumor segmentation and classification methods are discussed. This study describes the different types of brain tumor segmentation and classification techniques with its most important contributions. The survey of brain tumor segmentation and classification (BTSC) technique including the four main phases—preprocessing, feature extraction, segmentation, and classification—is discussed. The different types of BTSC techniques are listed, along with their great contributions. A review of recent articles on classifiers shows the eccentric features of classifiers for research.

CLCU-Net: Cross-level connected U-shaped network with selective feature aggregation attention module for brain tumor segmentation

Grid conditional random fields (CRFs) are widely applied in both natural and medical image segmentation tasks. However, they only consider the label coherence in neighborhood pixels or regions, which limits their ability to model long-range connections within the image and generally results in excessive smoothing of tumor boundaries. In this paper, we present a novel method for brain tumor segmentation in MR images based on fully-connected CRF (FC-CRF) model that establishes pairwise potentials on all pairs of pixels in the images. We employ a hierarchical approach to differentiate different structures of tumor and further formulate a FC-CRF model with learned data-driven prior knowledge of tumor core. The methods were evaluated on the testing and leaderboard set of Brain Tumor Image Segmentation Benchmark (BRATS) 2013 challenge. The precision of segmented tumor boundaries is improved significantly and the results are competitive compared to the start-of-the-arts.

Deformation-aware and reconstruction-driven multimodal representation learning for brain tumor segmentation with missing modalities

Precise tumor localization and sub-region identification are critical for disease diagnosis. However, current Weakly Supervised Semantic Segmentation (WSSS) methods for brain tumor segmentation are primarily single-class, neglecting differences between tumor sub-regions. We observed that when mainstream transformer-based WSSS methods are applied to multi-class brain tumor segmentation, they encounter two major challenges: sub-region discrimination errors and over-segmentation of small lesions. To address these challenges and advance multi-class WSSS methods for brain tumor analysis, this paper proposes Channel-gated Transformers with Affinity CAM (CTAC). CTAC first employs channel-gated multi-head self-attention to overcome the over-smoothing tendency of the transformer, thereby enhancing inter-class discriminability and improving the model's subclass differentiation capability. Then, CTAC uses multi-scale smoothed affinity to adaptively suppress low-confidence responses in the Class Activation Map (CAM), mitigating over-activation in the CAM, and alleviating the over-segmentation phenomena of small lesions. The proposed CTAC significantly outperformed the baseline method on the BraTS2021 glioma and BraTS2023-MEN meningioma datasets. On Brats2021, it achieved a multi-class mean IoU (mIoU) of 61.718%, an increase of 4.964 percentage points (pp), with the whole-tumor mIoU reaching 79.798% (+6.882 pp). On Brats2023-MEN, CTAC attained 72.887% mIoU (+4.676 pp) for multi-class segmentation and 75.394% (+7.839 pp) for whole-tumor. Furthermore, CTAC surpasses recent state-of-the-art methods. Code is available at https://github.com/yhan94-lab/CTAC.

This paper presents a comparative study of various classification methods in the application of automatic brain tumor segmentation. The data used in the study are 3D MRI volumes from MICCAI2016 brain tumor segmentation (BRATS) benchmark. 30 volumes are chosen randomly as a training set and 57 volumes are randomly chosen as a test set. The volumes are preprocessed and a feature vector is retrieved from each volume's four modalities (T1, T1 contrast-enhanced, T2 and Fluid-attenuated inversion recovery). The popular Dice score is used as an accuracy measure to record each classifier recognition results. All classifiers are implemented in the popular machine learning suit of algorithms, WEKA.

Brain tumour segmentation from MRI using superpixels based spectral clustering

Segmentation and classification of brain tumor are time-consuming and challenging chore in clinical image processing. Magnetic Resonance Imaging (MRI) offers more information related to human soft tissues that assists in diagnosing brain tumor. Precise segmentation of the MRI images is vital to diagnose brain tumor by means of computer-aided medical tools. Afterwards suitable segmentation of MRI brain tumor images, tumor classification is performed that is a hard chore owing to complications. Therefore, Gannet Aquila Optimization Algorithm_deep maxout network (GAOA_DMN) and GAOA_K-Net+speech enhancement generative adversarial network (GAOA_K-Net+Segan) is presented for classification and segmentation of brain tumor utilizing MRI images. Here, pre-processing phase performs noise removal from input image utilizing the Laplacian filter and also the region of interest (ROI) extraction is also carried out. Then, segmentation of brain tumor is conducted by K-Net+Segan, which is combined by Motyka similarity. However, K-Net+Segan for segmentation is trained by GAOA that is an amalgamation of Gannet Optimization Algorithm (GOA) and Aquila Optimizer (AO). From segmented image, features are extracted for performing classification phase. At last, brain tumor classification is conducted by DMN, which is tuned by GAOA and thus, output is obtained. Furthermore, GAOA_K-Net+Segan obtained better outcomes in terms of segmentation accuracy whereas devised GAOA_DMN achieved maximum accuracy, true negative rate (TNR) and true positive rate (TPR) of 92.7%, 94.5% and 91.5%.

Accurate and automated brain tumor segmentation using multi modal MR images is essential for the evaluation of the disease progression in order to improve disease diagnosis and treatment planning. We present a new fully automated method for high graded brain tumor segmentation combining sparse autoencoder and multimodal Fuzzy C-means clustering. The approach utilizes multimodal MRI contrast: T1, T2, FLAIR and T1c (contrast-enhanced) for 15 high graded glioma (HGG) subjects. The objective of the proposed study is to segment tumor tissues from HGG including edema and tumor core within edema. The segmentation was performed on the training data of the multimodal brain tumor image segmentation benchmark 2015. Sparse autoencoder, which is an unsupervised learning algorithm, was used to automatically learn features from unlabeled dataset of tumor in order to segment edema. Followed by edema segmentation, tumor core was segmented from edema using multimodal FCM clustering. Evaluating the performance of the segmentation results with the ground truth yields high dice score (DS) of 0.9866±0.01 and 0.9843±0.01 for edema and tumor core respectively and high Jaccard similarity (JS) of 0.9738±0.02 and 0.9692±0.02 for edema and tumor core respectively; showed high accuracy in segmenting the complex tumor structures from multi-contrast MR scans of HGG patients. We also compared our methodology in terms of segmentation efficiency with some recent techniques reported in proceedings of MICCAI-BRATS challenge 2015.

A brain tumor is one of the conditions in which a patient's brain develops abnormal cells. They are called tumors and there are several varieties of them. In this paper, brain tumor segmentation and detection methods are being developed that use images from an MRI series as input images to identify the type of tumor. The use of a convolutional neural network (CNN) model to recognize brain tumors from X-ray images pictures is described in this paper. This investigation was carried out with the help of Google Collab. The MRI images of brain tumors are categorized into glioma, meningioma, and pituitary, and no tumor is one such difficult problem. In this work, the datasets comprise 388 images for testing purposes. With an accuracy of 70%, the projected arrangement achieves a commendable performance. The successful conclusion demonstrates the suggested algorithm’s ability to classify brain tumors.

Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested. Simple thresholds and statistical methods are unable to adequately segment the various elements of the GBM, such as local contrast enhancement, necrosis, and edema. Most voxel-based methods cannot achieve satisfactory results in larger data sets, and the methods based on generative or discriminative models have intrinsic limitations during application, such as small sample set learning and transfer. A new method was developed to overcome these challenges. Multimodal MR images are segmented into superpixels using algorithms to alleviate the sampling issue and to improve the sample representativeness. Next, features were extracted from the superpixels using multi-level Gabor wavelet filters. Based on the features, a support vector machine (SVM) model and an affinity metric model for tumors were trained to overcome the limitations of previous generative models. Based on the output of the SVM and spatial affinity models, conditional random fields theory was applied to segment the tumor in a maximum a posteriori fashion given the smoothness prior defined by our affinity model. Finally, labeling noise was removed using "structural knowledge" such as the symmetrical and continuous characteristics of the tumor in spatial domain. The system was evaluated with 20GBM cases and the BraTS challenge data set. Dice coefficients were computed, and the results were highly consistent with those reported by Zikic et al. (MICCAI 2012, Lecture notes in computer science. vol 7512, pp 369-376, 2012). A brain tumor segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms.

In this paper, we propose an automated approach based on the geometric median shift algorithm over Riemannian manifolds, for the brain tumor detection and segmentation in magnetic resonance images (MRI). This approach is based on the geometric median, geodesic distance. We propose the median shift to overcome the limitation of mean which is not necessary a point in a set. The geodesic distance can describe data points distributed on a manifold, compared to the Euclidean distance, and produce efficient results for image analysis. Coupled with k-means algorithm, the proposed framework can cluster the brain image into tree regions (gray matter, white matter and cerebrospinal fluid) and abnormalities regions. We applied this approach to clustering the brain tissues and brain tumor segmentation, which is validated on a synthetic brain MRI. The obtained results using two datasets show the efficiency of the used algorithm validated qualitatively by the measurement of Dice Similarity Coefficient.