Study on MRI Brain Tumor Recognition and Segmentation Based on an Improved U-Net Network Model

A Validation Framework for Brain Tumor Segmentation

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 segmentation of brain tumors plays an important role in assisting doctors in clinical diagnosis and treatment. A brain tumor segmentation algorithm based on an improved U-Net network is proposed to address the shortcomings of traditional U-Net networks in brain tumor segmentation accuracy. Propose to combine the U-Net model with ResNeXt50, using ResNeXt50 as the encoder part of U-net, utilizing its powerful feature extraction capability, and then passing these features to the decoder part of U-net for segmentation. At the same time, adding channel attention mechanism makes the model pay more attention to important feature channels to improve segmentation performance. By introducing variable convolution, the model's adaptability to complex shapes in medical images can be increased, improving segmentation accuracy. This model was tested on the LGG Segmentation Dataset dataset, with Dice values as high as 94.66% and Iou values as high as 90.34%. Comparative experiments were conducted with other models, and the results showed that our model has better segmentation performance compared to other segmentation models, and can clearly and effectively segment skin lesion images.

State of the art survey on MRI brain tumor segmentation

Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy. Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data. We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as "better" quality (BQ) or "worse" quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts. For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models. Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.

Considering the clinical significance of brain tumor segmentation and the challenge of annotation scarcity, a self-supervised learning framework combining anatomical symmetry and cross-modal feature transfer (AnaCoMT+) is proposed. AnaCoMT+ innovatively integrates masked autoencoder and contrastive learning, which can extract biological structural features from annotated MRI images through a decoupled training strategy. AnaCoMT+ consists of a representation learning-based encoder, a self-supervised reconstruction head, a self-supervised projection head and a supervised segmentation head. The reconstruction head is employed to conduct cross-modal image reconstruction, thereby extracting the coordinated representations of multimodal images. The projection head is responsible for performing comparative learning of healthy brain regions and tumor regions, thereby enabling the model to focus on the feature expression of lesion areas. The novel design effectively resolves key limitations in current methods: (1) misalignment between pre-training targets and segmentation objectives, and (2) insufficient feature representation in semi-/self-supervised paradigms. Besides, in the construction of AnaCoMT+, in order to integrate and utilize multi-scale features, a multi-scale attention block (MSAB) and a hybrid attention block (HAB) are proposed, further improving the accuracy of brain tumor segmentation. Experimental results on the Brats 2019 dataset show that AnaCoMT+ can obtain an average Dice coefficient of 0.84, which is better than the existing methods.

HSA-net with a novel CAD pipeline boosts both clinical brain tumor MR image classification and segmentation

A Review on Convolutional Neural Networks for Brain Tumor Segmentation: Methods, Datasets, Libraries, and Future Directions

In this work, we propose a fully automatic brain tumor and edema segmentation technique in brain magnetic resonance (MR) images. Different brain tissues are characterized using the novel texture features such as piece-wise triangular prism surface area (PTPSA), multi-fractional Brownian motion (mBm) and Gabor-like textons, along with regular intensity and intensity difference features. Classical Random Forest (RF) classifier is used to formulate the segmentation task as classification of these features in multi-modal MRIs. The segmentation performance is compared with other state-of-art works using a publicly available dataset known as Brain Tumor Segmentation (BRATS) 2012 [1]. Quantitative evaluation is done using the online evaluation tool from Kitware/MIDAS website [2]. The results show that our segmentation performance is more consistent and, on the average, outperforms other state-of-the art works in both training and challenge cases in the BRATS competition.

Brain tumor are uncontrollable and abnormal cells in the brain. The incidence and mortality of brain tumors are very high. Among them, gliomas are the most common primary malignant tumors with different degrees of invasion. The segmentation of brain tumors is a prerequisite for disease diagnosis, surgical planning and prognosis. According to the characteristics of brain tumor data, we designed a multi-model fusion brain tumor automatic segmentation algorithm based on attention mechanism []. Our network architecture is slightly modified based on 3D U-Net []. At the same time, the attention mechanism was added to the 3D U-Net model. According to the patch size and attention mechanism in the training process, four independent networks are designed. Here, we use 64 × 64 × 64 and 128 × 128 × 128 patch sizes to train different sub-networks. Finally, the results of the four models in the label layer are combined to get the final segmentation results. This multi model fusion method can effectively improve the robustness of the algorithm. At the same time, the attention method can improve the feature extraction ability of the network and improve the segmentation accuracy. Our experimental study on the newly released brats data set (brats 2019) shows that our method accurately describes brain tumors.

This study aims to develop a multi-modal brain tumor segmentation technique based on a multi-scale feature extraction network to enhance the accuracy of brain tumor segmentation and assist in the clinical diagnosis of neurological diseases. Built upon the advanced U-Net architecture, the study designed a network framework with residual connections, downsampling and upsampling modules, and multi-branch combination mechanisms, achieving the extraction and integration of features from multiple scales. By integrating deep learning techniques such as depthwise separable convolutions, spatial pyramid pooling (SPP), and attention mechanisms, this paper innovatively proposes a multi-scale feature fusion strategy. The network model employs a composite loss function combining cross-entropy and Dice Loss, optimized through regularization methods and the SGD algorithm, with fine-tuning of hyperparameters achieved through grid search. Experiments used datasets including multi-modal medical images such as MRI, CT, PET, and underwent rigorous data preprocessing, such as image registration and normalization, to ensure the quality of input data. The multi-modal brain tumor segmentation experiments were evaluated comprehensively using metrics such as precision, recall rate, F1 score, and ROC curves through K-fold cross-validation. Experimental results show that the network model proposed in this article reaches an advanced level on all performance metrics, exhibiting a significant advantage, especially in processing multi-modal image data, and verifying its generalization ability. Statistical significance testing further confirmed the robustness of the model, providing a powerful tool for future efficient clinical diagnosis and treatment. In conclusion, the study summarizes the innovative points and comparative advantages and looks forward to the pursuit of real-time segmentation performance and optimization of data fusion strategies.

Segmenting brain tumors automatically using MR data is crucial for disease investigation and monitoring. Due to the aggressive nature and diversity of gliomas, well-organized and exact segmentation methods are used to classify tumors intra-tumorally. The proposed technique uses a Gray Level Co-occurrence matrix extraction of features approach to strip out unwanted details from the images. In comparison with the current state of the art, the accuracy of brain tumor segmentation was significantly improved using Convolutional Neural Networks, which are frequently used in the field of biomedical image segmentation. By merging the results of two separate segmentation networks, the proposed method demonstrates a major but simple combinatorial strategy that, as a direct consequence, yields much more precise and complete estimates. A U-Net and a Three-Dimensional Convolutional Neural Network. These networks are used to break up images into their component parts. Following that, the prediction was constructed using two distinct models that were combined in a number of ways. In comparison to existing state-of-the-art designs, the proposed method achieves the mean accuracy (%) of 99.40, 98.46, 98.29, precision (%) of 99.41, 98.51, 98.35, F-Score (%) of 99.4, 98.29, 98.46 and sensitivity (%) of 99.39, 98.41, 98.25 for the whole tumor, enhanced tumor, tumor core on the validation set, respectively.

In brain tumor diagnosis, clinicians integrate their medical knowledge and brain magnetic resonance imaging (MRI) scans to obtain the nature and pathological characteristics of brain tumors and to decide on treatment options. However, manually detecting and segmenting brain tumors in today's brain MRI, where a large number of MRI scans taken for each patient, is tedious and subjected to inter and intra observer detection and segmentation variability. As result a number of methods have been proposed in recent years to fill this gap, but still there is no commonly accepted automated technique by clinicians to be used in clinical floor due to accuracy and robustness issues. In our approach, an automatic brain tumor detection and segmentation framework that consists of techniques from skull stripping to detection and segmentation of brain tumors is proposed with fuzzy Hopfield neural network as its final tumor segmentation technique. Through preprocessing, image fusion and initial tumorous slice classification, the final hybrid intelligent fuzzy Hopfield neural network algorithm based tumor segmentation, and tumor region detection and extraction is achieved. The performance of the proposed framework is evaluated on various MR images including simulated and real, normal and tumorous. Quantitatively the method is validated against available ground truth using commonly used validation metrics. The final segmentation mean and standard deviation result in Jaccard similarity index, Dice similarity score, sensitivity and specificity are 0.8569+/-0.0896, 0.9186+/-0.0638, 0.9480+/-0.0402 and 0.9917+/-0.0387 respectively. Quantitative and qualitative segmentation result indicates the potential of the proposed framework.

Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification and segmentation using MR images, which helps solve the difficulties of brain tumor identification efficiently and accurately. Our methodology is based on the accurate and efficient selection of tumorous areas. That reduces computational complexity and time. We have validated the designed experimental setup on a standard dataset, BraTS 2020. We used binary evaluation matrices based on Dice Similarity Coefficient (DSC) and Mean Average Precision (mAP). The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed method. The suggested approach attained an average DSC of 0.92 and mAP 0.92 for 10 patients, while on the whole dataset, the scores are DSC 0.89 and mAP 0.90. The following results clearly show the performance efficiency of the proposed methodology.

Unsupervised brain tumor segmentation using a symmetric-driven adversarial network