Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine

The use of 3D Magnetic Resonance Imaging (MRI) has attracted growing attention for the purpose of diagnosis and treatment planning of intraocular ocular cancers. Precise segmentation of such tumors are highly important to characterize tumors, their progression and to define a treatment plan. Along this line, automatic and effective segmentation of tumors and healthy eye anatomy would be of great value. The major challenge to this end however lies in the disease variability encountered over different populations, often imaged under different acquisition conditions and high heterogeneity of tumor characterization in location, size and appearance. In this work, we consider the Retinoblastoma disease, the most common eye cancer in children. To provide automated segmentations of relevant structures, a multi-sequences MRI dataset of 72 subjects is introduced, collected across different clinical sites with different magnetic fields (3T and 1.5T), with healthy and pathological subjects (children and adults). Using this data, we present a framework to segment both healthy and pathological eye structures. In particular, we make use of a 3D U-net CNN whereby using four encoder and decoder layers to produce conditional probabilities of different eye structures. These are further refined using a Conditional Random Field with Gaussian kernels to maximize label agreement between similar voxels in multi-sequence MRIs. We show experimentally that our approach brings state-of-the-art performances for several relevant eye structures and that these results are promising for use in clinical practice.

The aim of this paper is to present a new approach for improving deformable models and especially the Level Set Method (LSM) for segmentation and extraction of brain tumors in MRI (Magnetic Resonance Imaging) with more accuracy, the contribution of this work is to exploit thermal behavior of brain tumors for correcting level set contours. Human Body temperature distribution is an indicator of health condition, the brain tumor cells generate more heat than normal brain cells due to their higher metabolism and their vascular dilation. Heat distribution in human body is modeled using Pennes BioHeat Transfer Equation (PBHTE) solved by Finite Volume Method (FVM), and with the inverse analysis using Genetic Algorithm (GA) we will estimate the size and location of brain tumor, with this way Level Set Method extracts tumors contours with more accuracy and efficiency. To our knowledge, this is the first approach which introduces thermal analysis to improve the accuracy of segmentation and extraction of tumors in MRI images.

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.

A Validation Framework for Brain Tumor Segmentation

The area of medical imaging specialization, specifically in the context of brain tumor segmentation, has long been challenged by the inherent complexity and variability of brain structures. Traditional segmentation methods often struggle to accurately differentiate between the diverse types of tissues within the brain, such as white matter, grey matter, and cerebrospinal fluid, leading to suboptimal results in tumor identification and delineation. These limitations necessitate the development of more advanced and precise segmentation techniques to enhance diagnostic accuracy and treatment planning. In response to these challenges, the proposed study introduces a novel segmentation approach that combines the Grey Wolf Optimization approach and the Cuckoo Search approach within a Fuzzy C-Means (FCM) framework. The integration of GWO and CS is designed to leverage their respective strengths in optimizing the segmentation of brain tissues. This hybrid approach was rigorously tested across multiple Magnetic Resonance Imaging (MRI) datasets, demonstrating significant enhancements over existing segmentation methods. The study observed a 4,9 % improvement in accuracy, 3,5 % increase in precision, 4,5 % higher recall, 3,2 % less delay, and 2,5 % better specificity in tumor segmentation. The implications of these advancements are profound. By achieving higher precision and accuracy in brain tumor segmentation, the proposed method can substantially aid in early diagnosis and accurate staging of brain tumors, eventually leading to more effective treatment planning and improved patient outcomes. Furthermore, the integration of GWO and CS within the FCM process sets a new benchmark in medical imaging, paving the way for future investigation in the field of study

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.

The major goal of this paper is to isolate tumor region from nontumor regions and the estimation of tumor volume. Accurate segmentation is not an easy task due to the varying size, shape and location of the tumor. After segmentation, volume estimation is necessary in order to accurately estimate the tumor volume. By exactly estimating the volume of abnormal tissue, physicians can do excellent prognosis, clinical planning and dosage estimation. This paper describes a new Euclidean Similarity factor (ESF) based active contour model with deep learning for segmenting the tumor region into complete, core and enhanced tumor portions. Initially, the ESF considers the spatial distances and intensity differences of the region automatically to detect the tumor region. It preserves the image details but removes the noisy details. Then, the 3D Convolutional Neural Network (3D CNN) segments the tumor by automatically extracting spatiotemporal features. Finally, the extended shoelace method estimates the volume of the tumor accurately for [Formula: see text]-sided polygons. The simulation result achieves a high accuracy of 92% and Jaccard index of 0.912 and computes the tumor volume with effective performance than existing approaches.

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%.

Brain tumor segmentation from MRI data is an important but challenging task. This paper presents an efficient and fully automatic brain tumor segmentation technique. The proposed technique includes a fuzzy C-means (FCM) based preprocessing to enhance the quality of T1-weighted coronal MR images, a fast bounding box (FBB) detection algorithm to locate a rectangle around tumor, and a new dynamic snake using modified Hausdorff distance (MHD) for the final tumor extraction.

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

Many clinical trials of Glioblastoma (GBM) therapies use changes in tumor burden to determine treatment response. Often, tumor size is estimated from bi-orthogonal lines placed to cover the largest cross-sectional extent of the lesion. This method is simple, rapid, and widely used. However, it is plagued by low accuracy. Consequently, many are developing more accurate algorithms to directly label, or “segment” the 3D extent of GBM tumors in MRI exams. The BRain Tumor Segmentation (BRATS) challenge allows objective comparison of these algorithms. BRATS includes anonymized MRI exams from patients with GBMs. Each exam includes a “true” tumor boundary determined from manual outlining by experts. A “leader board” of the most accurate algorithms is maintained on the BRATS website. However, leading algorithms often require long computation times (> 80 minutes in at least one case). We present a new method to segment brain tumors from multi-spectral MRI exams. Our approach leverages the speed of a graphical processing unit (GPU) level set algorithm to allow rapid and simple brain tumor segmentation. We implement a new data term in the level set speed function. This term eliminates the need to store MR exams on the GPU memory, as in prior approaches. Instead, a fixed size data and seed dependent lookup table is employed. This allows multi-spectral MR exams that are too large to fit on the GPU memory to be processed efficiently. We evaluated our method on the BRATS 2013 data. Our algorithm achieved an overall accuracy (Dice coefficient) on whole GBM segmentation of 83.5%. This is comparable to top performing algorithms. The average segmentation time was 30.8 seconds. Current limitations include the need for a user to place seed regions in each exam. Future work will focus on automating this step using machine-learning based approaches.

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.

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.

Segmentation of brain tumor from magnetic resonance imaging is a challenging and time-consuming task due to the unpredictable appearance of tumor tissue in practical applications. In this paper we propose a novel level-set-based model for tumor segmentation from multi-modality magnetic resonance imaging. We formulate the tumor segmentation on pixel-level with three classes: tumor, edema and healthy tissue. First, we detect abnormal regions by using a region-based active contour model on T2-weighted images with fluid-attenuated inversion recovery modality, and a variational level set formulation is applied locally to approximate the image intensities on two sides of the contour. In the second stage, we distinguish the edema and tumor tissues in the abnormal regions based on the contrast enhancement T1 modality. Compared with traditional one-modality methods, our model can better represent the specific tissue of tumor in a simple way. The validation experiments on synthetic and clinical brain magnetic resonance images demonstrate the effectiveness and simplicity of the proposed method 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.