О сегментации опухолей головного мозга по МРТ-изображениям с применением методов глубокого обучения

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.

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

BackgroundThe brain tumor is the growth of abnormal cells inside the brain. These cells can be grown into malignant or benign tumors. Segmentation of tumor from MRI images using image processing techniques started decades back. Image processing based brain tumor segmentation can be divided in to three categories conventional image processing methods, Machine Learning methods and Deep Learning methods. Conventional methods lacks the accuracy in segmentation due to complex spatial variation of tumor. Machine Learning methods stand as a good alternative to conventional methods. Methods like SVM, KNN, Fuzzy and a combination of either of these provide good accuracy with reasonable processing speed. The difficulty in processing the various feature extraction methods and maintain accuracy as per the medical standards still exist as a limitation for machine learning methods. In Deep Learning features are extracted automatically in various stages of the network and maintain accuracy as per the medical standards. Huge database requirement and high computational time is still poses a problem for deep learning. To overcome the limitations specified above we propose an unsupervised dual autoencoder with latent space optimization here. The model require only normal MRI images for its training thus reducing the huge tumor database requirement. With a set of normal class data, an autoencoder can reproduce the feature vector into an output layer. This trained autoencoder works well with normal data while it fails to reproduce an anomaly to the output layer. But a classical autoencoder suffer due to poor latent space optimization. The Latent space loss of classical autoencoder is reduced using an auxiliary encoder along with the feature optimization based on singular value decomposition (SVD). The patches used for training are not traditional square patches but we took both horizontal and vertical patches to keep both local and global appearance features on the training set. An Autoencoder is applied separately for learning both horizontal and vertical patches. While training a logistic sigmoid transfer function is used for both encoder and decoder parts. SGD optimizer is used for optimization with an initial learning rate of .001 and the maximum epochs used are 4000. The network is trained in MATLAB 2018a with a processor capacity of 3.7 GHz with NVIDIA GPU and 16 GB of RAM.ResultsThe results are obtained using a patch size of 16 × 64, 64 × 16 for horizontal and vertical patches respectively. In Glioma images tumor is not grown from a point rather it spreads randomly. Region filling and connectivity operations are performed to get the final tumor segmentation. Overall the method segments Meningioma better than Gliomas. Three evaluation metrics are considered to measure the performance of the proposed system such as Dice Similarity Coefficient, Positive Predictive Value, and Sensitivity.ConclusionAn unsupervised method for the segmentation of brain tumor from MRI images is proposed here. The proposed dual autoencoder with SVD based feature optimization reduce the latent space loss in the classical autoencoder. The proposed method have advantages in computational efficiency, no need of huge database requirement and better accuracy than machine learning methods. The method is compared Machine Learning methods Like SVM, KNN and supervised deep learning methods like CNN and commentable results are obtained.

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.

Brain tumor segmentation in medical image analysis is a challenging task. Deep learning techniques have recently shown promise in resolving a variety of computer vision problems, such as semantic segmentation and image classification. Brain MRI (magnetic resonance imaging) requires precise brain image segmentation for effective, rapid diagnosis and treatment planning. However, it is quite difficult to manually segment the brain image rapidly and accurately in low-quality, noisy images. This paper proposes a U-Net and combined attention mechanism-based method. This research enhances the segmentation of images of tumors in the brain using modified U-net. Traditional U-net segmentation techniques are still widely used in the medical field, but they have a number of limitations when dealing with small targets and fuzzier boundaries. To address this issue, we made the following modifications to U-net: We propose attention mechanisms to assist the network in concentrating on important regions. The multiscale feature fusion strategy improves the efficacy of network segmentation at various scales. Cross-entropy loss function and data augmentation improve the performance of the network further. Our method was validated using the Brats2019 dataset. The experimental results demonstrate that our proposed methodology exhibits superior speed and efficiency compared to existing techniques in the context of brain image segmentation. The dice coefficients for the multiple branch TS-U-net model were 0.876, 0.868, and 0.814 in the tumor subregions of WT, TC, and ET, respectively. This exemplifies the feasibility and potential of our methodology for the segmentation of medical images.

A Validation Framework for Brain Tumor Segmentation

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.

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

Highly accurate tumor segmentation and classification are required to treat the brain tumor appropriately. Brain tumor segmentation (BTS) approaches can be categorized into manual, semi-automated, and full-automated. The deep learning (DL) approach has been broadly deployed to automate tumor segmentation in therapy, treatment planning, and diagnosing evaluation. It is mainly based on the U-Net model that has recently attained state-of-the-art performances for multimodal BTS. This paper demonstrates a literature review for BTS using U-Net models. Additionally, it represents a common way to design a novel U-Net model for segmenting brain tumors. The steps of this DL way are described to obtain the required model. They include gathering the dataset, pre-processing, augmenting the images (optional), designing/selecting the model architecture, and applying transfer learning (optional). The model architecture and the performance accuracy are the two most important metrics used to review the works of literature. This review concluded that the model accuracy is proportional to its architectural complexity, and the future challenge is to obtain higher accuracy with low-complexity architecture. Challenges, alternatives, and future trends are also presented.

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.

With the advent of new technologies in the field of medicine, there is rising awareness of biomechanisms, and we are better able to treat ailments than we could earlier. Deep learning has helped a lot in this endeavor. This paper deals with the application of deep learning in brain tumor segmentation. Brain tumors are difficult to segment automatically given the high variability in the shapes and sizes. We propose a novel yet simple fully convolutional network (FCN) which results in competitive performance and faster runtime than state-of-the-art model. Using the database provided for the Brain Tumor Segmentation (BraTS) challenge by the Medical Image Computing and Computer Assisted Intervention (MICCAI) society, we are able to achieve dice scores of 0.83 in the whole tumor region, 0.75 in the core tumor region and 0.72 in the enhancing tumor region, while our method is about 18 times faster than the state-of-the-art.

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 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.