MetaMorph: Learning Metamorphic Image Transformation with Appearance Changes

Identification of brain tumors and accurate grading at an early stage are crucial in cancer diagnosis, as a timely diagnosis can increase the chances of survival. Considering the challenges and risks of tumor biopsies, noninvasive imaging procedures such as Magnetic Resonance Imaging (MRI) are extensively used in analyzing brain tumors. Recent advances in the field of medical imaging with deep learning using three dimensional (3D) MRI is aiding the clinical experts significantly in the diagnosis of brain tumor. In this study, three BraTS MRI datasets named BraTS 2018, BraTS 2019 and BraTS 2020 are employed to classify brain tumor into high-grade glioma (HGG) and low-grade glioma (LGG) where each of the datasets contains four different sequences of 3D MRI brain images named T1-weighted MRI (T1), T1-weighted MRI with contrast enhancement (T1ce), T2-weighted MRI (T2), and Fluid Attenuated Inversion Recovery (FLAIR) for a single patient. This research is composed of two approaches where, in the first part, we propose a hybrid deep learning model named TimeDistributed-CNN-LSTM (TD-CNN-LSTM) combining 3D Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) where each layer of the architecture is wrapped with a TimeDistributed function. The objective of developing the hybrid model is to consider all the four 3D MRI sequences of each patient as a single input data because every sequence contains necessary information on tumor that can have an impact on improving cancer detection performance. However, interpreting all the MRI sequences together with optimal performance especially in 3D is quite challenging. Therefore, the model is developed with optimal configuration based on highest accuracy performing ablation study for layer architecture and hyper-parameters. In the second part, a 3D CNN model is trained respectively with each of the MRI sequences to compare the performance with TD-CNN-LSTM model. In this regard, for both of the models, BraTS 2018 and BraTS 2019 are combined to increase the number of images and used as train dataset where BraTS 2020 dataset is employed as the test dataset. Moreover, before training the models the datasets is preprocessed to ensure the highest performance. Our results of these two approaches demonstrate that the TD-CNN-LSTM network outperforms 3D CNN achieving the highest test accuracy of 98.90%. Later, to evaluate the performance consistency, the TD-CNN-LSTM model is evaluated with K-fold cross validation using different k values. The approach of putting together all the MRI sequences at a time using hybrid CNN-LSTM approach with good generalization capability to classify brain tumor can be used effectively in future Computer Aided Diagnosis (CAD) based research which can aid radiologists in medical diagnostics.

Objectives:The objective of our study is to determine whether the addition of3-dimensional (3D) MRI to standard MRI sequences is comparable to 3D CT scanevaluation of glenoid and humeral bone loss in glenohumeral instability.There is increasing understanding of the importance of the glenoid, as wellas humeral bone loss in outcomes and surgical decision making in patientswith shoulder instability. Although magnetic resonance imaging (MRI) isperformed as standard of care for soft tissue evaluation of shoulderinstability, CT scan remains the gold standard in bony measurements.Standard MRI evaluation has been shown to inaccurately measure bone loss.However, additional CT scans result in increased financial, time, andionizing radiation cost to the patient.Methods:Eighteen patients who presented with glenohumeral instability wereprospectively enrolled and received both MRI and CT within 1 week of eachother. The MRI included an additional sequence (VIBE) which underwentpost-processing for reformations (Figure 1a). The addition of a VIBEprotocol, on average is an addition 4 to 4.5minutes in the scanner. CT dataalso underwent 3D post-processing (Figure 1b) and therefore each patient hadfour imaging modalities (2D CT, 2D MRI, 3D CT reformats, and 3D MRIreformats). Each sequence underwent the following measurements from twoseparate reviewers: glenoid defect, glenoid defect percent, humeral defect,humeral defect percentage (Figure 2a and 2b), and evaluation of glenoidtrack and version. Paired t-tests were used to assess differences betweenimaging modalities and chi squared for glenoid track. Intra-observer andinter-observer reliability were evaluated. Bland-Altman tests were alsoperformed to assess the agreement between CT and MRI. In addition, wedetermined cost of each imaging modality at our single institution.Results:3D MRI measurements for glenoid and humeral bone loss measurements werecomparable to 3D CT (Table 1). There were no significant differences forglenoid defect size and percentage, or humeral defect size and percentage(P>0.05) (Table 2). Bland-Altman analysis demonstrated strong agreementwith small measurement errors for 3D CT and 3D MRI percentage glenoid boneloss. There was also no difference in evaluation for determining on vs offtrack between any of the imaging modalities. Inter- and intra-raterreliability was good to excellent for all CT and MRI measurements (r >=0.7).Conclusions:3D MRI measurements for bone loss in glenohumeral instability throughutilization of VIBE sequence were equivalent to 3D CT. At our institution,the costs of MRI with 3D reconstruction was 1.67x cheaper than MRI and CTwith 3D reconstructions. 3D MRI may be a useful adjuvant to standard MRIsequences to allow concurrent soft tissue and accurate assessment of glenoidand humeral bone loss in glenohumeral instability.Figure 1a.(3D MRI glenoid reformation) and lb (3D CT glenoid reformation):Glenoid measurement for 3D reconstruction of MRI and CT usingsagittal en face of the glenoid. A circle was placed over theinferior portion of the glenoid using margins as a guide for thecircle. The glenoid height line (blue) runs from the supraglenoidtubercle to the medial aspect of the inferior base. Glenoid boneloss size (orange) and circle diameter (green) was quantified withglenoid height line marking the center of the circle. Percentglenoid bone loss was determined as [(glenoid bone loss size/circlediameter)* 100]. Glenoid track was determined through equation[(0.83 * circle diameter) - glenoid bone loss size]Figure 2a.(2D CT) and 2b (3D CT): 2a: Measurement of Hill Sachs maximal defecton axial 2D CT recorded in mm. 2b: Measurement of humeral defectpercentage from the 3D reformats using sagittal projection with bestfit circle. A line (blue) was placed to determine maximal humeralhead height (A) then perpendicular line (green) to measure residualhumeral head (B) and therefore bone defect (A-B)(orange). Percentbone loss was determined through [(A-B)/A)*100]Table 1:Mean 2D and 3D CT and MRI measurementsTable 2:Paired t-test Glenoid and Humeral Bone Loss Measurements.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient’s brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

Early diagnosis and proper grouping of tumors in the brain are critical for successful therapy and positive outcomes for patients. This work proposes a complete technique for identifying brain tumors that employ sophisticated artificial intelligence methodologies and achieve an accuracy rate of 97.18%. The work makes use of the brain tumor magnetic resonance imaging (MRI) collection in Kaggle, which has 723 MRI scans classified as glioma, meningioma, pituitary tumor, and no tumor. These images are initially preprocessed, which includes scaling to a homogeneous size normalizing, and removal of noise to ensure uniformity and clarity. To improve the information set, generative adversarial networks (GANs) are used to perform data augmentation, producing artificial pictures that improve the database variety and resilience. To achieve exact cancer localization, the U-Net construction, recognized for its encoder-decoder design and skip links, is used to divide up tumor areas across images generated by MRI. The image segments are then input into gated recurrent units (GRUs), to analyze a collection of features to capture periods and differences between segments. The last classification is accomplished using an entirely linked layer and then a softmax stimulation, which provides the tumors classes. This method helps for medical experiments and clinical methods.

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.

Brain tumor images segmentation plays a crucial role in the auxiliary diagnosis of disease, treatment planning and surgical navigation. In order to accurately segment brain tumor images, this paper proposes an automatic brain tumor Magnetic Resonance Imaging (MRI) image segmentation algorithm based on the U-net model. Firstly, based on samples preprocessing, loss function and optimization algorithm, a U-net baseline model with optimal parameters structure which is suitable for segmentation task is constructed. Secondly, a feature recombination layer, i.e. a 1×1 convolutional layer, is added to the baseline model to linearly recombine the upper layer features, enrich extracted features, reduce network parameters, and improve the segmentation results. The algorithm is validated and evaluated on the Brain Tumor Segmentation Challenge (Brats) 2015 challenge training dataset. The experimental results show that the proposed algorithm has strong competitiveness compared with the existing brain tumor MRI image segmentation algorithm.

The human brain is one of the body's most major organs. If there are problems within the human brain, they may cause serious consequences, and even endangers the human life. One of the most fatal diseases for humans is a brain tumor. In the old days, tumor detection was done manually by doctors through reading magnetic resonance images, which might not be time efficient, and sometimes may even produce inaccurate results. Nowadays, with the development of science and technology, Artificial Intelligence (AI) is present in many fields in human life, including medical field. Tumor detection with AI is one of the applications that technology changes human life. The Convolution Neural Network (CNN), a prominent algorithm in deep learning, is widely employed in tumor identification. In this study, a CNN model is proposed. Over 7000 brain tumor magnetic resonance images, including glioma, meningioma, no tumor and pituitary are used in this study. The images are also preprocessed to improve the accuracy of the proposed models. In this study, the well-known VGG16 model, which is a pretrained deep learning model, is utilized to compare with the proposed model. The proposed model and the VGG16 model are trained and evaluated using both the original (uncropped) images and preprocessed (cropped) images. The results of the experiment indicate that the suggested model exceeds the VGG16 model in the light of loss and accuracy.

We study the problem of classifying brain tumors as benign or malignant using information from magnetic resonance (MR) imaging and magnetic resonance spectroscopy (MRS) to assist in clinical diagnosis. The proposed approach consists of several steps including segmentation, feature extraction, feature selection, and classification model construction. Using an automated segmentation technique based on fuzzy connectedness we accurately outline the tumor mass boundaries in the MR images so that further analysis concentrates on these regions of interest (ROIs). We then apply a concentric circle technique on the ROIs to extract features that are utilized by the classification algorithms. To remove redundant features, we perform feature selection where only those features with discriminatory information (among classes) are used in the model building process. The involvement of MRS features further improves the classification accuracy of the model. Experimental results demonstrate the effectiveness of the proposed approach in classifying brain tumors in MR images.

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.

The accuracy of three-dimensional (3D) brain tumor image segmentation is of great significance to brain tumor diagnosis. To enhance the accuracy of segmentation, this study proposes an algorithm integrating a cascaded anisotropic fully convolutional neural network (FCNN) and the hybrid level set method. The algorithm first performs bias field correction and gray value normalization on T1, T1C, T2, and fluid-attenuated inversion recovery magnetic resonance imaging (MRI) images for preprocessing. It then uses a cascading mechanism to perform preliminary segmentation of whole tumors, tumor cores, and enhancing tumors by an anisotropic FCNN based on the relationships among the locations of the three types of tumor structures. This simplifies multiclass brain tumor image segmentation problems into three binary classification problems. At the same time, the anisotropic FCNN adopts dense connections and multiscale feature merging to further enhance performance. Model training is respectively conducted on the axial, coronal, and sagittal planes, and the segmentation results from the three different orthogonal views are combined. Finally, the hybrid level set method is adopted to refine the brain tumor boundaries in the preliminary segmentation results, thereby completing fine segmentation. The results indicate that the proposed algorithm can achieve 3D MRI brain tumor image segmentation of high accuracy and stability. Comparison of the whole-tumor, tumor-core, and enhancing-tumor segmentation results with the gold standards produced Dice similarity coefficients (Dice) of 0.9113, 0.8581, and 0.7976, respectively.

ChatGPT-4o's Performance in Brain Tumor Diagnosis and MRI Findings: A Comparative Analysis with Radiologists.

Rotator cuff tear shape characterization: a comparison of two-dimensional imaging and three-dimensional magnetic resonance reconstructions

Brain tumor image classification is one of the predominant tasks of brain image processing. The three‐class brain tumor classification becomes a trivial task for researchers as each tumor exhibit distinct characteristics. Existing classification models use deep neural networks and suffer from high computational cost. We have proposed an eight‐layer average‐pooling convolutional neural network to address three‐class brain tumor classification. The proposed model uses three convolution blocks along with a dense layer and a softmax layer. We have utilized N‐adam optimizer with a sparse‐categorical cross‐entropy loss function to improve the learning rate. The proposed model has been evaluated using a dataset consists of 3064 brain tumor magnetic resonance images. The proposed model outperforms state‐of‐the‐art models with 97.42% accuracy and takes lesser computation time than its competitive models.

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.

The need to make timely and accurate diagnoses of brain diseases has posed challenges to computer-aided diagnosis systems. In this field, advances in deep learning techniques play an important role, as they carry out processes to extract relevant anatomical and functional characteristics of the tissues to classify them. In this paper, the study of various architectures of convolutional neural networks (CNN) is presented, with the aim of classifying three types of brain tumors in high-contrast magnetic resonance (MR) images. The architectures of the present study were VGG16, ResNet50, Xception, whose implementations are defined in the Keras framework. The evaluation of these architectures were preceded by data augmentation techniques and transfer learning, which improved the effectiveness of the training process, thanks to the use of pre-trained models with the ImageNet dataset. The VGG16 architecture was the one with the best performance, with an accuracy of 98.04%, followed by ResNet50 with 94.89%, and finally, Xception with 92.18%.