3D Kronecker Convolutional Feature Pyramid for Brain Tumor Semantic Segmentation in MR Imaging

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 detection and segmentation from Magnetic Resonance Imaging (MRI) images is being one of the emerging fields in the biomedicine. A formidable undertaking in brain tumor surgery, medical care, treatment programme and quantitative assessment of MRI images is to precisely diagnose its location and extent. Recently, the convolutional neural network (CNN) based detection and segmentation method on brain tumor MRI images is being one of the emerging fields in the medical imaging as an automatic clinic treatment and evaluation solution. In this article, we put forward a brand new quadruplet loss in CNN framework, which achieves higher accuracy in brain tumor detection and segmentation than other pairwise loss and triplet loss methods. By applying the proposed quadruplet loss to the original L2Net CNN architecture leads to a more compact descriptor named QuadrupletNet. From our experiments, QuadrupletNet shows higher performance than other state-of-the-art loss functions e.g., the Triplet loss, as indicated in experiments on Multimodal Brain Tumor Image Segmentation (BRATS 2018) datasets, and on our own collected MRI brain tumor datasets (named MBTD).

Brain tumour segmentation from MRI using superpixels based spectral clustering

A Validation Framework for Brain Tumor Segmentation

Intracranial tumors are commonly known as brain tumors, which can be life-threatening in severe cases. Magnetic resonance imaging (MRI) is widely used in diagnosing brain tumors because of its harmless to the human body and high image resolution. Due to the heterogeneity of brain tumor height, MRI imaging is exceptionally irregular. How to accurately and quickly segment brain tumor MRI images is still one of the hottest topics in the medical image analysis community. However, according to the brain tumor segmentation algorithms, we could find now, most segmentation algorithms still stay in two-dimensional (2D) image segmentation, which could not obtain the spatial dependence between features effectively. In this study, we propose a brain tumor automatic segmentation method called scSE-NL V-Net. We try to use three-dimensional (3D) data as the model input and process the data by 3D convolution to get some relevance between dimensions. Meanwhile, we adopt non-local block as the self-attention block, which can reduce inherent image noise interference and make up for the lack of spatial dependence due to convolution. To improve the accuracy of convolutional neural network (CNN) image recognition, we add the “Spatial and Channel Squeeze-and-Excitation” Network (scSE-Net) to V-Net. The dataset used in this paper is from the brain tumor segmentation challenge 2020 database. In the test of the official BraTS2020 verification set, the Dice similarity coefficient is 0.65, 0.82, and 0.76 for the enhanced tumor (ET), whole tumor (WT), and tumor core (TC), respectively. Thereby, our model can make an auxiliary effect on the diagnosis of brain tumors established.

Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification and segmentation using MR images, which helps solve the difficulties of brain tumor identification efficiently and accurately. Our methodology is based on the accurate and efficient selection of tumorous areas. That reduces computational complexity and time. We have validated the designed experimental setup on a standard dataset, BraTS 2020. We used binary evaluation matrices based on Dice Similarity Coefficient (DSC) and Mean Average Precision (mAP). The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed method. The suggested approach attained an average DSC of 0.92 and mAP 0.92 for 10 patients, while on the whole dataset, the scores are DSC 0.89 and mAP 0.90. The following results clearly show the performance efficiency of the proposed methodology.

Glioblastoma is an aggressive type of cancer that can develop in the brain or spinal cord. Magnetic Resonance Imaging (MRI) is key to diagnosing and tracking brain tumors in clinical settings. Brain tumor segmentation in MRI is required for disease diagnosis, surgical planning, and prognosis. As these tumors are heterogeneous in shape and appearance, their segmentation becomes a challenging task. The performance of automated medical image segmentation has considerably improved because of recent advances in deep learning. Introducing context encoding with deep CNN models has shown promise for semantic segmentation of brain tumors. In this work, we use a 3D UNet-Context Encoding (UNCE) deep learning network for improved brain tumor segmentation. Further, we introduce epistemic and aleatoric Uncertainty Quantification (UQ) using Monte Carlo Dropout (MCDO) and Test Time Augmentation (TTA) with the UNCE deep learning model to ascertain confidence in tumor segmentation performance. We build our model using the training MRI image sets of RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021. We evaluate the model performance using the validation and test images from the BraTS challenge dataset. Online evaluation of validation data shows dice score coefficients (DSC) of 0.7787, 0.8499, and 0.9159 for enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The dice score coefficients of the test datasets are 0.6684 for ET, 0.7056 for TC, and 0.7551 for WT, respectively.

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.

Accurately separating healthy tissue from tumorous regions is crucial for effective diagnosis and treatment planning based on magnetic resonance imaging (MRI) data. Current manual detection methods rely heavily on human expertise, so MRI-based segmentation is essential to improving diagnostic accuracy and treatment outcomes. The purpose of this paper is to compare the performance of detecting brain tumors from MRI images through segmentation using an unmodified and modified U-Net architecture from deep neural network (DNN) that has been modified by adding batch normalization and dropout on the encoder layer with and without the freeze layer. The study utilizes a public 2D brain tumor dataset containing 3064 T1-weighted contrast-enhanced images of meningioma, glioma, and pituitary tumors. Model performance was evaluated using intersection over union (IoU) and standard metrics such as precision, recall, f1-score, and accuracy across training, validation, and testing stages. Statistical analysis, including ANOVA and Duncan's multiple range test, was conducted to determine the significance of performance differences across the architectures. Results indicate that while the modified architectures show improved stability and convergence, the freeze layer model demonstrated superior IoU and efficiency, making it a promising approach for more accurate and efficient brain tumor segmentation. The comparison of the three methods revealed that the modified U-Net architecture with a freeze layer significantly reduced training time by 81.72% compared to the unmodified U-Net while maintaining similar performance across validation and testing stages. All three methods showed comparable accuracy and consistency, with no significant differences in performance during validation and testing.

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.

Enhanced performance of Dark-Nets for brain tumor classification and segmentation using colormap-based superpixel techniques

Brain tumor is known as the most aggressive tumor among all other tumors, which leads to short life span of the patients. Therefore to improve the health of oncological patient, treatment planning is a critical stage. The most common method used worldwide is MRI (Magnetic resonance imaging) to understand the behaviour of the tumors. MRI takes huge amount of data to properly define the criticality of tumor. The time taken during Manual segmentation can be reduced by utilizing exact quantitative technique for the future clinical practices. This research is focused towards studying the different techniques used in past by various researchers. This work would summarize the benefits, limitations and future opportunity to detect brain tumor more precisely.

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) images is important for proper medication. Manual segmentation may be erroneous and a computer-aided method is recommended for precise segmentation which is also challenging due to the contrast level of MRI images. This research work proposes to utilize a parametric active contour model (PACM)-based deformable snake model to segment brain tumors from MRI images. Conventional PACM model prerequisites some initial points for its initialization which may have a time-consuming issue. The main contribution of this paper is to modify the PACM algorithm, so that it can predict its initial points around the region of interest (ROI) from the given minimum (at least three) initial points. This proposed method aids PACM to find the initial contour points automatically to start the deformable mechanism. Furthermore, different parameters of the PACM algorithm are optimized for the segmentation by check and trial method. The proposed method is applied to different shapes of brain tumors inside the MRI images and found satisfactory segmentation outcomes. Furthermore, the proposed algorithm reports the number of total pixels inside the segmented area. Therefore, we hope that this proposal will help to find the area of critically shaped brain tumor in an MRI image.

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.

—Segmenting tumor from MRI images is an essential but time consuming manual duty. Performing an automatic segmentation is a defying task since different forms of tumor tissue exist for diverse patients and in many cases the tumor is similar to the normal tissue. Various studies proposed earlier to handle the issue of precisely segmenting the tumor but they discard the degradations and their effect to the precision of the segmentation. This article provides a more precise segmentation process through the use of appropriate pre-processing algorithms. The authors studied many enhancement and restoration algorithms and selected the NL-means, Laplacian filter and histogram equalization to be used as preprocessing techniques. Experimental results showed that using a suitable preprocessing scheme would produce a better segmentation process. Index Terms — Medical image processing, Tumor segmentation, MRI images, Non-local Means, Histogram equalization, Laplacian filter. I. INTRODUCTION A tumor is a mass of tissue that grows out of control of the normal forces that regulates growth. Brain tumor has a higher mortality rate when compared to more common types of cancer. This underlines the need of accurate prognosis of brain tumor in health care industry [1]. MRI has become a widely-used method of high-quality medical imaging, especially in brain imaging [2]. Image segmentation refers to the process of partitioning a digital image into multiple segments, also known as super pixels. Image segmentation is an important process in computer vision and image processing, which divides an image into a number of disjoint regions such that the pixels have high similarity in order to use its different fields; science, medical, agriculture and industrial fields [3]. Besides, Magnetic Resonance Imaging (MRI) images are known to be corrupted by noise [4], blur [5] and low contrast artifact [6]. Therefore, this article discusses the issue of achieving a better segmentation through the use of proper image restoration and enhancement techniques. Moreover, the accurate and automatic segmentation of a brain tumor on MRI image is of great interest for assessing tumor growth and treatment responses, enhancing computer-assisted surgery, and constructing tumor growth models [7]. For those reasons, an automatic and reliable tumor segmentation method would be a useful tool since the medical image segmentation plays an instrumental role in clinical diagnosis [8]. The rest of the article sections are organized as the following: in section two, a brief elucidation about some related studies is presented as the used literature review. Moreover, detailed explanations about the employed algorithms are delivered in section three as a methodology. Also, the results of the proposed methodology are illustrated in section four as the experimental results. Likewise, a detailed discussion about the use of certain algorithms and its results is deliberated in section five. Finally, a brief closure about this article is provided in section six as a conclusion. II. LITERATURE REVIEW In this section, three recent studies are briefly described. The first study was presented by [9] which proposed a texture based analysis to identify irregularity in the brain and automatic segmentation using seeded region growing segmentation. The method of tumor detection based on the texture of the MRI is proposed to segment the abnormality from the regular adjacent, although small quantities misclassified, but the segmentation result is precise. Moreover, [10] suggested a strong technique for automatic detection of tumor for brain MRI images. He also proposed a new morphological technique which is used for the stripped skull region from the brain images. Detecting tumor precisely based on seeded region growing segmentation on an improved single seed point selection algorithm is