Brain Tumor Segmentation Meets Efficiency: Res-UNet Improved by Attention Mechanisms and Quantization

Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.

Brain tumors are complex clinical lesions with diverse morphological characteristics, making accurate segmentation from MRI scans a challenging task. Manual segmentation by radiologists is time-consuming and susceptible to human error. Consequently, automated approaches are anticipated to accurately delineate tumor boundaries and quantify tumor burden, addressing these challenges efficiently. The presented work integrates a convolutional block attention module (CBAM) into a deep learning architecture to enhance the accuracy of MRI-based brain tumor segmentation. The deep learning network is built upon a VGG19-based U-Net model, augmented with depthwise and pointwise convolutions to improve feature extraction and processing efficiency during brain tumor segmentation. Furthermore, the proposed framework enhances segmentation precision while simultaneously incorporating tumor area measurement, making it a comprehensive tool for early-stage tumor analysis. Several qualitative assessments are used to assess the performance of the model in terms of tumor segmentation analysis. The qualitative metrics typically analyze the overlap between predicted tumor masks and ground truth annotations, providing information on the segmentation algorithms' accuracy and dependability. Following segmentation, a new approach is used to compute the extent of segmented tumor areas in MRI scans. This involves counting the number of pixels within the segmented tumor masks and multiplying by their area or volume. The computed tumor areas offer quantifiable data for future investigation and clinical interpretation. In general, the proposed methodology is projected to improve segmentation accuracy, efficiency, and clinical relevance compared to existing methods, resulting in better diagnosis, treatment planning, and monitoring of patients with brain tumors.

Learning global dependencies based on hierarchical full connection for brain tumor segmentation

Background Magnetic resonance imaging (MRI) is essential for brain tumor diagnosis. Deep learning models, such as Residual Network 50 Version 2 (ResNet50V2), have demonstrated strong performance in tumor classification. However, integrating attention mechanisms may further enhance diagnostic accuracy. This study evaluates the impact of different attention mechanisms on a ResNet50V2-based MRI tumor classification model for distinguishing between meningioma, glioma, pituitary tumors, and cases with no tumor. Methods A ResNet50V2-based model was trained on 3,096 annotated MRI scans from a publicly available dataset on Kaggle. Five model configurations were evaluated: baseline ResNet50V2, Squeeze-and-Excitation (SE), Convolutional Block Attention Module (CBAM), Self-Attention (SA), and Attention Gated Network (AGNet). Performance was assessed using accuracy, area under the receiver operating characteristic curve (AUC), precision, and recall. Two-proportion Z-tests were conducted to compare classification accuracies among models. Results The SE-enhanced model achieved the highest classification performance, with an accuracy of 98.4% and an AUC of 1.00, outperforming the base ResNet50V2 (92.6%) and other attention-based frameworks (CBAM: 93.5%, SA: 91.6%, AGNet: 94.2%). Compared to the baseline model, the SE model also demonstrated improved meningioma and pituitary tumor classification(Z = 2.485, p = 0.013 and Z = 2.423, p = 0.015, respectively). Additionally, the SE model demonstrated superior precision and recall across all tumor classes. Conclusion Incorporating attention mechanisms significantly improves MRI-based tumor classification, with SE proving to be the most effective. These findings suggest that SE-enhanced models may improve diagnostic accuracy in both research and clinical applications. Future research should explore hybrid attention mechanisms, such as transformer-based models, and their broader applications in medical imaging.

Brain tumor segmentation with advanced nnU-Net: Pediatrics and adults tumors

Brain tumours within the skull can lead to serious health issues. The rapid and accurate detection and segmentation of tumour regions allow patients to receive appropriate treatment at an early stage, increasing their chances of recovery and survival. Various medical imaging methods, such as Magnetic Resonance Imaging (MRI), Positron and digital pathology, Emission Tomography (PET), Computed Tomography (CT) are used for the detection of brain tumours. Nowadays, with advancing technology and hardware, concepts like artificial intelligence and deep learning (DL) are becoming increasingly popular. Many artificial intelligence methods are also being utilized in studies on brain tumour segmentation. This paper proposes a 3D U-Net DL model for brain tumour segmentation. The training and testing processes are carried out on the Brain Tumour Segmentation (BraTS) 2020 dataset, which is widely used in the literature. As a result, an Intersection over Union (IoU) score of 0.81, a dice score of 0.87 and a pixel accuracy of 0.99 are achieved. The proposed model has the potential to assist experts in diagnosing the disease and developing appropriate treatment plans, thanks to its ability to segment brain tumours quickly and with high accuracy.

The application of deep learning in the field of medical imaging has become increasingly widespread, greatly promoting the advancement and development of Magnetic Resonance Imaging (MRI) brain tumor detection and segmentation techniques. Therefore, a comprehensive review of deep learning-based methods for MRI brain tumor detection and segmentation was conducted. This review introduces the basic concepts of brain tumors and MRI brain tumor detection and segmentation, discusses the specific applications and typical methods of deep learning in MRI brain tumor detection and segmentation, and analyzes and compares the performance and advantages and disadvantages of different methods. Additionally, representative brain tu-mor segmentation dataset (BraTS) and its evaluation metrics are introduced, upon which the performance of various deep learning-based brain tumor segmentation methods on the BraTS 2019-2022 dataset is compared. Lastly, the challenges and future development trends in deep learning-based MRI brain tumor detection and segmentation methods are summarized and anticipated.

Science and technology has had a huge impact in the field of medicine leading to more accurate and preventive diagnosis, and treatment. Detecting brain tumors in early stages is essential for timely treatment of patients. Automatic segmentation of brain tumors is a challenging task as tumors vary in shapes and size. In this paper, we propose a fully automatic novel deep learning architecture for brain tumor segmentation named ATSNet. The network provides an end-to-end solution for feature extraction and brain tumor segmentation on Magnetic Resonance Images. Our proposed model uses an encoder-decoder architecture, employing residual modules for tackling gradient dispersion and uses skip connections for better feature map synthesis. The network utilizes attention gates (AG) to tackle the variability of brain tumors. Performance metrics such as dice score, precision, recall and intersection-over-union (IoU) have been used to evaluate and benchmark our model against those reported in literature. We have evaluated our model using the k-fold cross-validation approach. Our analysis also includes an ablation study on our model to identify important parts of the architecture by their effect on performance for optimizing the model.

In order to improve the segmentation effect of brain tumor images and address the issue of feature information loss during convolutional neural network (CNN) training, we present an MRI brain tumor segmentation method that leverages an enhanced U-Net architecture. First, the ResNet50 network was used as the backbone network of the improved U-Net, the deeper CNN can improve the feature extraction effect. Next, the Residual Module was enhanced by incorporating the Convolutional Block Attention Module (CBAM). To increase characterization capabilities, focus on important features and suppress unnecessary features. Finally, the cross-entropy loss function and the Dice similarity coefficient are mixed to compose the loss function of the network. To solve the class unbalance problem of the data and enhance the tumor area segmentation outcome. The method's segmentation performance was evaluated using the test set. In this test set, the enhanced U-Net achieved an average Intersection over Union (IoU) of 86.64% and a Dice evaluation score of 87.47%. These values were 3.13% and 2.06% higher, respectively, compared to the original U-Net and R-Unet models. Consequently, the proposed enhanced U-Net in this study significantly improves the brain tumor segmentation efficacy, offering valuable technical support for MRI diagnosis and treatment.

Background and objectiveAccurate diagnosis of brain tumors significantly impacts patient prognosis and treatment planning. Traditional diagnostic methods primarily rely on clinicians’ subjective interpretation of medical images, which is heavily dependent on physician experience and limited by time consumption, fatigue, and inconsistent diagnoses. Recently, deep learning technologies, particularly Convolutional Neural Networks (CNN), have achieved breakthrough advances in medical image analysis, offering a new paradigm for automated precise diagnosis. However, existing research largely focuses on single-task modeling, lacking comprehensive solutions that integrate tumor segmentation with classification diagnosis. This study aims to develop a multi-task deep learning model for precise brain tumor segmentation and type classification.MethodsThe study included 485 pathologically confirmed cases, comprising T1-enhanced MRI sequence images of high-grade gliomas, metastatic tumors, and meningiomas. The dataset was proportionally divided into training (378 cases), testing (109 cases), and external validation (51 cases) sets. We designed and implemented BrainTumNet, a deep learning-based multi-task framework featuring an improved encoder-decoder architecture, adaptive masked Transformer, and multi-scale feature fusion strategy to simultaneously perform tumor region segmentation and pathological type classification. Five-fold cross-validation was employed for result verification.ResultsIn the test set evaluation, BrainTumNet achieved an Intersection over Union (IoU) of 0.921, Hausdorff Distance (HD) of 12.13, and Dice Similarity Coefficient (DSC) of 0.91 for tumor segmentation. For tumor classification, it attained a classification accuracy of 93.4% with an Area Under the ROC Curve (AUC) of 0.96. Performance remained stable on the external validation set, confirming the model’s generalization capability.ConclusionThe proposed BrainTumNet model achieves high-precision diagnosis of brain tumor segmentation and classification through a multi-task learning strategy. Experimental results demonstrate the model’s strong potential for clinical application, providing objective and reliable auxiliary information for preoperative assessment and treatment decision-making in brain tumor cases.

Comprehensive benchmarking of CNN-based tumor segmentation methods using multimodal MRI data

A significant rise in brain cancer cases over last few decades, insists early tumor detection, which is typically accomplished using vision-based computing and magnetic resonance imaging (MRI). Despite advances in deep learning for medical imaging, achieving accurate and timely tumor segmentation remains challenging due to issues like class imbalance, gradient vanishing, and premature convergence. Furthermore, limited works focus on simultaneous segmentation of the whole tumor and its substructures, which is essential for comprehensive diagnosis and treatment planning. To overcome these gaps, this paper proposes a Trans-Semantic Residual Deep Structure for Scalable Brain Tumor Segmentation namely BResUNet which is a combination of U-Net and ResUNet. This multi-stage deep network included sub-components like Pre-trained Activation Residual Layers (PARUL), attention gates, deep supervision, etc. The annotations followed by U-Net segmentation enable alleviating class imbalance, while Z-score normalization avoids the over-fitting problem. Architecturally, BResUNet is designed to perform segmentation of the whole tumor region as well as allied sub-structures as a multi-task problem. The ability to learn semantic contextual features as well as morphological features enable BResUNet to achieve optimal brain tumor segmentation results. To enable a scalable and time-efficient solution, BResUNet is executed over TensorFlow Distributed Computing Architecture with Multi-Task Mirroring (TD-CAM). Simulation results reveals the BResUNet’s higher performance with 95.37% accuracy, and 92.29% sensitivity, ensuring its applicability in real-time brain tumor diagnosis.

Brain tumor segmentation technology plays a pivotal role in the process of diagnosis and treatment of MRI brain tumors. It helps doctors to locate and measure tumors, as well as develop treatment and rehabilitation strategies. Recently, MRI brain tumor segmentation methods based on U-Net architecture have become popular as they largely improve the segmentation accuracy by applying skip connection to combine high-level feature information and low-level feature information. Meanwhile, researchers have demonstrated that introducing attention mechanism into U-Net can enhance local feature expression and improve the performance of medical image segmentation. In this work, we aim to explore the effectiveness of a recent attention module called attention gate for brain tumor segmentation task, and a novel Attention Gate Residual U-Net model, i.e., AGResU-Net, is further presented. AGResU-Net integrates residual modules and attention gates with a primeval and single U-Net architecture, in which a series of attention gate units are added into the skip connection for highlighting salient feature information while disambiguating irrelevant and noisy feature responses. AGResU-Net not only extracts abundant semantic information to enhance the ability of feature learning, but also pays attention to the information of small-scale brain tumors. We extensively evaluate attention gate units on three authoritative MRI brain tumor benchmarks, i.e., BraTS 2017, BraTS 2018 and BraTS 2019. Experimental results illuminate that models with attention gate units, i.e., Attention Gate U-Net (AGU-Net) and AGResU-Net, outperform their baselines of U-Net and ResU-Net, respectively. In addition, AGResU-Net achieves competitive performance than the representative brain tumor segmentation methods.

Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors