Memory-Efficient Cascade 3D U-Net for Brain Tumor Segmentation

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.

For patients with brain tumors, effectively utilizing the complementary information between multimodal medical images is crucial for accurate lesion segmentation. However, effectively utilizing the complementary features across different modalities remains a challenging task. To address these challenges, we propose a modal feature supplement network (MFSNet), which extracts modality features simultaneously using both a main and an auxiliary network. During this process, the auxiliary network supplements the modality features of the main network, enabling accurate brain tumor segmentation. We also design a modal feature enhancement module (MFEM), a cross‐layer feature fusion module (CFFM), and an edge feature supplement module (EFSM). MFEM enhances the network performance by fusing the modality features from the main and auxiliary networks. CFFM supplements additional contextual information by fusing features from adjacent encoding layers at different scales, which are then passed into the corresponding decoding layers. This aids the network in preserving more details during upsampling. EFSM improves network performance by using deformable convolution to extract challenging boundary lesion features, which are then used to supplement the final output of the decoding layer. We evaluated MFSNet on the BraTS2018 and BraTS2021 datasets. The Dice scores for the whole tumor, tumor core, and enhancing tumor regions were 90.86%, 90.59%, 84.72%, and 92.28%, 92.47%, 86.07%, respectively. This validates the accuracy of MFSNet in brain tumor segmentation, demonstrating its superiority over other networks of similar type.

GCAUNet: A group cross-channel attention residual UNet for slice based 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%.

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

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

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.

Brain tumour segmentation from MRI using superpixels based spectral clustering

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.

A Validation Framework for Brain Tumor Segmentation

Brain tumors are one of the most common neurological complications that affect millions each year. Current studies have focused only on either the segmentation of brain tumors or healthy anatomical regions. Unfortunately, there is a lack of research on whole brain segmentation of patients with existing tumors. The segmentation of these regions is cruical for understanding patient pathologies. Due to the distortions in the brain caused by tumors, whole-brain segmentation models trained on healthy brains will not perform accurately on diseased ones. To address this issue, this paper proposes a novel unified 3D nnU-Net model for the segmentation of both brain tumor and whole-brain structures for the first time. Training a model on datasets that include both healthy and cancerous brain regions could enhance the generalizability of machine learning (ML) models for performing both whole-brain segmentation and tumor segmentation tasks. Currently, there are no datasets that are publicly available for this purpose. This paper proposes to address this challenge by augmenting data from OASIS-1 and BraTS2023 to train the baseline model. The model successfully achieves a Dice Similarity Coefficient (DSC) of 0.814 for whole brain structures and 0.874 for tumor segmentation, demonstrating its effectiveness across diverse regions.

An automatic segmentation of brain tumors from medical images is significant for diagnosis and treatment planning. Magnetic resonance imaging (MRI) is widely used by surgeons, radiologists, and medical specialists for brain segmentation. To enhance the reliability and security of the segmentation process, this study introduces a blockchain‐based Recursive Residual Blocks and Pyramid Pooling Segmentation Network (RP‐Segnet) for brain tumor segmentation using MRI. In the proposed framework, blockchain technology is employed to ensure the integrity and traceability of the parameters of the model by encrypting and verifying layer‐specific data, such as weights and biases. This prevents unauthorized tampering and enables secure handling of model data, thereby supporting robust and trustworthy segmentation. Initially, input MRI is acquired from the dataset and is transferred to the preprocessing step, wherein the unwanted images are eliminated by employing Gaussian filtering. Once the preprocessing is completed, the brain image is transferred to the brain tumor segmentation module, which separates the tumor region from the background of the tumor image, which is performed using the proposed RP‐Segnet. The RP‐Segnet is designed by the formation of Recursive residual blocks and a Pyramid pooling Network (RP‐Net) and Segmentation Network (SegNet). Moreover, the efficacy of the proposed RP‐Segnet for brain tumor segmentation is determined based on evaluation measures, such as segmentation accuracy, dice coefficient, and Jaccard similarity, and it has attained maximal values of 92.04%, 95.07%, and 95.03%.

Accurate volumetric segmentation of 3D medical imaging modalities is critical for therapy planning and clinical diagnosis, particularly for brain tumor delineation. Traditional convolutional neural network (CNN)-based architectures face challenges while capturing global contextual information and modeling long-range dependencies in complex 3D volumetric data, limiting their segmentation performance. Transformer-based models have emerged as promising alternatives to CNNs for such tasks, addressing their limitations in capturing global spatial dependencies. We propose 3D-ViT-UNet, a novel U-shaped vision transformer (ViT)-based encoder-decoder architecture for end-to-end volumetric brain tumor segmentation. The model employs 3D Window Multi-Head Self-Attention (3D-W-MSA) to capture local features and a 3D Dilated-Window Multi-Head Self-Attention (3D-DW-MSA) to capture global features while reducing computational complexity. Moreover, for preserving absolute and relative positional information and preventing permutation equivalence limitation in transformers, a dynamic position encoding strategy is integrated. The proposed model demonstrates state-of-the-art (SOTA) performance for brain tumor segmentation on the BraTS 2020 dataset. It achieves a superior average Dice Similarity Coefficient (DSC) of 84.81% and a Hausdorff Distance (HD) of 4.87 mm with reduced computational complexity compared to existing methods. Also, an improvement in delineation of tumor boundaries and accurate segmentation across modalities is demonstrated through the qualitative results. Extensive quantitative and qualitative evaluations highlight the capability of 3D-ViT-UNet to achieve high accuracy with a smaller model size and lower FLOPs, making it an effective and efficient solution for clinical applications involving volumetric brain tumor segmentation.

Segmentation of brain tumors in multi-parametric magnetic resonance imaging facilitates quantitative analysis crucial for clinical trials and personalized patient care. This significantly influences clinical decision-making, encompassing diagnosis and prognosis and enhancing patient outcomes. The brain tumor segmentation (BraTS) challenge, in its 2023 edition, extended to a cluster of competitions incorporating multiple tumor types. Now, in conjunction with IEEE ISBI 2024, BraTS organizes its Generalizability Across Tumors (BraTS-GoAT) challenge. In this paper, we introduce a deep-learning-based ensemble strategy involving three state-of-the-art segmentation models. Furthermore, we also introduce a novel adaptive post-processing method, based on a cross-validated tumor-specific threshold search, designed to output enhanced accurate segmentations, ensuring generalizability across various tumor types. The evaluation of our proposed method on validation cases resulted in lesion-wise Dice scores of 0.842, 0.854, 0.872 and lesion-wise 95th-percentile Hausdorff Distance scores of 29.46, 24.67, 25.22 for the enhancing tumor, tumor core, and whole tumor, respectively.

CDSG-SAM: A cross-domain self-generating prompt few-shot brain tumor segmentation pipeline based on SAM