U-Net-Based Models towards Optimal MR Brain Image Segmentation

A hybrid approach for multi modal brain tumor segmentation using two phase transfer learning, SSL and a hybrid 3DUNET

Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep learning, particularly 3D U-Net architectures, has revolutionised medical image analysis by leveraging volumetric data to capture spatial context, enhancing segmentation accuracy. This paper reviews brain tumour segmentation methods, emphasising 3D U-Net advancements. We analyse contributions from the Brain Tumour Segmentation (BraTS) challenges (2014-2023), highlighting key improvements and persistent challenges, including tumour heterogeneity, limited annotated data, varied imaging protocols, computational constraints, and model generalisation. Unlike previous reviews, we synthesise these challenges, proposing targeted research directions: enhancing model robustness through domain adaptation and multi-institutional data sharing, developing lightweight architectures for clinical deployment, integrating multi-modal and clinical data, and incorporating explainability techniques to build clinician trust. By addressing these challenges, we aim to guide future research toward developing more robust, generalisable, and clinically applicable segmentation models, ultimately improving patient outcomes in neuro-oncology.

Segmenting brain tumors in magnetic resonance imaging (MRI) scans has been a challenging issue with medical imaging, because of the great diversity in tumors regarding their sizes, shapes, and textures. Traditional 3D convolutional neural networks (3D-CNNs) have been shown to be very good at extracting local features, but bad at capturing relationships across large distances. To overcome this drawback, the current study offers a hybrid deep-learning architecture, which is the 3D Dual-Path Transformer Residual Attention U-Net (3D DPT-RAUNet) to realize an accurate segmentation of the brain-tumor. The architecture proposed will combine two orthogonal modules: 3D Residual Attention U-Net branch that will focus on fine-grained learning of local features, and Transformer-based Dual-Path Encoder which will focus on learning global spatial cues in the entire MRI volume. Tumor-relevant regions are selectively highlighted by means of the attention mechanism, and gradient propagation stabilized by residual connections is achieved by the model to achieve better convergence. Experiments with the BraTS2023 dataset using substantial preprocessing include bias field correction, intensity normalisation, and patch extraction show that the 3D DPT - RAUNet obtains a mean Dice similarity coefficient of 0.957 which is significantly better than current 3D U-Net and Transformer-based models and is at a low cost of about 18 - percent. The findings highlight the superior performance, strength, and clinical practicability of the hybrid architecture in automating the analysis of brain-tumors.

Many current and state-of-the-art deep learning models for accurate image segmentation are based on the U-Net architecture, a convolutional neural network designed for biomedical applications. Despite its widespread adoption in the medical imaging community, U-Net has two major limitations. First, due to its deep structure and the large number of filters used, the number of parameters and Floating-Point Operations Per Second (FLOPS) are high. This results in high computational complexity and demands a large memory size, making real-time implementation and deployment of U-Net models challenging. Second, the base U-Net model only uses a single type of kernel (i.e., 3×3) throughout the network for all convolution operations. Feature extraction using a single spatial extent throughout the network is suitable if the size, location, and shape of the salient regions remain static in all the images of a dataset, which is not necessarily the case in medical imaging. To address these two limitations, we propose an Efficient Multi-Encoder-Decoder based UNet (EMED-UNet), a novel architecture for efficient medical image segmentation. We evaluated our network on four medical imaging datasets: Montgomery County, Shenzhen CXR, COVID-19 CT LS, and BraTS (Brain Tumor Segmentation) dataset. EMED-UNet outperforms the U-Net and its variants in terms of accuracy, with around 77% reduction in parameters, a 60% reduction in FLOPS, and a 79.2% reduction in memory usage (all as compared to U-Net). The results demonstrate that EMED-UNet is a lightweight and accurate model for image segmentation that substantially improves upon the U-Net base model and is more feasible to deploy, given its decreased computational cost.

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.

MIRAU-Net: An improved neural network based on U-Net for gliomas segmentation

1 - Framework for segmentation, optimization, and recognition of multivariate brain tumors

Segmentation of a brain tumor is one of the most difficult tasks in the analysis of medical images. The purpose of brain tumor segmentation is to create an accurate outline of brain tumor areas. Gliomas are the most common type of brain tumors. Diagnosis of patients with this disease is based on the analysis of the results of magnetic resonance imaging and segmentation of the tumor boundaries manually. However, due to the time-consuming nature of the manual segmentation process and errors, there is a need for a fast and reliable automatic segmentation algorithm. In recent years, deep learning methods have shown promising effectiveness in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of methods based on deep learning have been applied to segmentation of brain tumors, and promising results have been achieved. The article proposes a hybrid method for solving the problem of segmentation of brain tumors based on its MRI images based on the U-Net architecture, the encoder of which uses a model of a deep convolutional neural network pre-trained on a set of ImageNet images. Among such models were used VGG16, VGG19, MobileNetV2, Inception, ResNet50, EfficientNetb7, InceptionResnetV2, DenseNet201, DenseNet121. Based on the hybrid method, the TL-U-Net model was implemented, and numerical experiments were carried out to train it with different encoder models for segmentation of brain tumors based on its MRI images. Computer experiments on a set of MRI images of the brain showed the effectiveness of the proposed approach, the best encoder model turned out to be the neural network Densenet121, which provided indicators of segmentation accuracy MeanIoU=90.34%, MeanDice=94.33%, accuracy=94.17%. The obtained estimates of segmentation accuracy are comparable or exceed similar estimates obtained by other researchers.

BackgroundMagnetic Resonance Imaging (MRI) is a cornerstone in diagnosing brain tumors. However, the complex nature of these tumors makes accurate segmentation in MRI images a demanding task.ObjectiveAccurate brain tumor segmentation remains a critical challenge in medical image analysis, with early detection crucial for improving patient outcomes.MethodsTo develop and evaluate a novel UNet-based architecture for improved brain tumor segmentation in MRI images. This paper presents a novel UNet-based architecture for improved brain tumor segmentation. The UNet model architecture incorporates Leaky ReLU activation, batch normalization, and regularization to enhance training and performance. The model consists of varying numbers of layers and kernel sizes to capture different levels of detail. To address the issue of class imbalance in medical image segmentation, we employ focused loss and generalized Dice (GDL) loss functions.ResultsThe proposed model was evaluated on the BraTS'2020 dataset, achieving an accuracy of 99.64% and Dice coefficients of 0.8984, 0.8431, and 0.8824 for necrotic core, edema, and enhancing tumor regions, respectively.ConclusionThese findings demonstrate the efficacy of our approach in accurately predicting tumors, which has the potential to enhance diagnostic systems and improve patient outcomes.

In this paper, we look at how to design and build a system to find tumors using 2 Convolutional Neural Network (CNN) models. With the help of digital image processing and deep Learning, we can make a system that automatically diagnoses and finds different diseases and abnormalities. The tumor detection system may include image enhancement, segmentation, data enhancement, feature extraction, and classification. These options are set up so that the CNN model can give the best results. During the training phase, the learning rate is used to change the weights and bias. The learning rate also changes the weights. One Epoch is when all of the training images are shown to the model. As the training data may be very large, the data in each epoch are split into batches. Every epoch has a training session and a test session. After each epoch, the weights are changed based on how fast the CNN is learning. This is done with the help of optimization algorithms. The suggested technique uses the anticipated mean intersection over union value to identify failure instances in addition to forecasting the mean intersection over union. This paper talks about how to separate brain tumors from magnetic resonance images of patients taken from "Brain web." Using basic ideas of digital image processing, magnetic resonance images are used to extract and find tumors using a hybrid method. In this paper, the proposed algorithm is applied with the help of MATLAB. In medical image processing, brain tumor segmentation is an important task. The goal of this paper is to look at different ways to divide brain tumors using magnetic resonance imaging. Recently, automatic segmentation using deep learning methods has become popular because these methods get the best results and are better at solving this problem than others. Deep learning methods can also be used to process and evaluate large amounts of magnetic resonance imaging image data quickly and objectively. A classification method based on a convolution neural network is also added to the proposed scheme to make it more accurate and cut down on the amount of time it takes to do the calculations. Also, the results of the classification are given as images of a tumor or a healthy brain. The training is 98.5% correct. In the same way, both the validation accuracy and validation loss are high.

Dual examiner consistency learning with dynamic receptive fields and class-balance refinement for Barely-supervised brain tumor segmentation

Brain tumor segmentation using neural networks presents challenges in accurately capturing diverse tumor shapes and sizes while maintaining real-time performance. Additionally, addressing class imbalance is crucial for achieving accurate clinical results. To tackle these issues, this study proposes a novel N-shaped lightweight network that combines multiple feature pyramid paths and U-Net architectures. Furthermore, we ingeniously integrate hybrid attention mechanisms into various locations of depth-wise separable convolution module to improve efficiency, with channel attention found to be the most effective for skip connections in the proposed network. Moreover, we introduce a combination loss function that incorporates a newly designed weighted cross-entropy loss and dice loss to effectively tackle the issue of class imbalance. Extensive experiments are conducted on four publicly available datasets, i.e., UCSF-PDGM, BraTS 2021, BraTS 2019, and MSD Task 01 to evaluate the performance of different methods. The results demonstrate that the proposed network achieves superior segmentation accuracy compared to state-of-the-art methods. The proposed network not only improves the overall segmentation performance but also provides a favorable computational efficiency, making it a promising approach for clinical applications.

Brain tumour segmentation is critical in medical image analysis, facilitating diagnosis and treatment planning in neurosurgery. Brain tumour segmentation with supervised learning shows robust results in medical imaging; however, it requires a sufficient amount of annotated data for effective learning. It is important to detect boundaries of tumour subregions accurately in fine-grained segmentation. We propose a novel approach that uses a unique dual-decoder architecture, focusing on edge identification and segmentation accuracy enhancement. Utilising a dual-decoder 3D-UNet model, we prioritise accuracy and fine-grained details in tumour segmentation and introduce an additional tumour edge detection task, aiming to move beyond traditional single-decoder approaches. Incorporating a 3D SimSiam network as the self-supervised pretraining technique, we aim to address the limitation of annotated data and enhance the segmentation accuracy. Our model surpasses many supervised variants of U-net architectures and self-supervised approaches, highlighting the importance of edge detection in tumour segmentation. The proposed approach enhances segmentation accuracy by showing an accuracy of 98.1% and provides critical boundary details for clinical decision-making. Visualisations of segmentation and edge masks further validate the effectiveness of the proposed method.

The rapid development of medical practices and imaging technology tools creates substantial growth in the amount of medical image data each year in our present era. This research aims to develop a hybrid approach that integrates Machine Learning (ML) and Deep Learning (DL) techniques to enhance the accuracy and reliability of medical image classification for diagnostic purposes. Medical imaging data complexity and growing volume serve as the research motivation, which leads to an investigation of standalone ML or DL limitations and their combination into a single framework. The medical image processing starts with normalization, then noise reduction, and continues to grayscale conversion before performing histogram equalization. This research uses VGG16 and ResNet50 alongside MobileNet and InceptionV3 for feature extraction, then applies ten different ML algorithms, including SVM and MLP, and Ran-dom Forest, for classification. Five public medical image datasets from Kaggle are used: COVID-19 chest X-rays, melanoma skin lesions, pneumonia chest X-rays, acute stroke facial images, and various eye diseases. Hybrid models display superior performance compared to stand-alone ML or DL models based on accuracy, precision, recall, and F1-score evaluation measures. Multiple datasets demonstrate that the MobileNet+MLP combination de-livers the most accurate results, which demonstrates its reliable and efficient performance. The developed AI diagnostic tool presents a scalable system alongside accuracy and interpretability to enhance clinical decision outcomes.

The number of medical imaging devices is quickly and steadily rising, generating an increasing amount of image records day by day. The number of qualified human experts able to handle this data cannot follow this trend, so there is a strong need to develop reliable automatic segmentation and decision support algorithms. The Brain Tumor Segmentation Challenge (BraTS), first organized seven years ago, provoked a strong intensification of the development of brain tumor detection and segmentation algorithms. Beside many others, several ensemble learning solutions have been proposed lately to the above mentioned problem. This study presents an evaluation framework developed to evaluate the accuracy and efficiency of these algorithms deployed in brain tumor segmentation, based on the BraTS 2016 train data set. All evaluated algorithms proved suitable to provide acceptable accuracy in segmentation, but random forest was found the best, both in terms of precision and efficiency.