Self-supervised wavelet-based attention network for semantic segmentation of MRI brain tumor

To determine the appropriate treatment plan for patients, radiologists must reliably detect brain tumors. Despite the fact that manual segmentation involves a great deal of knowledge and ability, it may sometimes be inaccurate. By evaluating the size, location, structure, and grade of the tumor, automatic tumor segmentation in MRI images aids in a more thorough analysis of pathological conditions. Due to the intensity differences in MRI images, gliomas may spread out, have low contrast, and are therefore difficult to detect. As a result, segmenting brain tumors is a challenging process. In the past, several methods for segmenting brain tumors in MRI scans were created. However, because of their susceptibility to noise and distortions, the usefulness of these approaches is limited. Self-Supervised Wavele- based Attention Network (SSW-AN), a new attention module with adjustable self-supervised activation functions and dynamic weights, is what we suggest as a way to collect global context information. In particular, this network’s input and labels are made up of four parameters produced by the two-dimensional (2D) Wavelet transform, which makes the training process simpler by neatly segmenting the data into low-frequency and high-frequency channels. To be more precise, we make use of the channel attention and spatial attention modules of the self-supervised attention block (SSAB). As a result, this method may more easily zero in on crucial underlying channels and spatial patterns. The suggested SSW-AN has been shown to outperform the current state-of-the-art algorithms in medical image segmentation tasks, with more accuracy, more promising dependability, and less unnecessary redundancy.Web of Science235art. no. 271

Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand

Gliomas are brain tumors composed of different highly heterogeneous histological subregions. Image analysis techniques to identify relevant tumor substructures have high potential for improving patient diagnosis, treatment and prognosis. However, due to the high heterogeneity of gliomas, the segmentation task is currently a major challenge in the field of medical image analysis. In the present work, the database of the Brain Tumor Segmentation (BraTS) Challenge 2018, composed of multimodal MRI scans of gliomas, was studied. A segmentation methodology based on the design and application of convolutional neural networks (CNNs) combined with original post-processing techniques with low computational demand was proposed. The post-processing techniques were the main responsible for the results obtained in the segmentations. The segmented regions were the whole tumor, the tumor core, and the enhancing tumor core, obtaining averaged Dice coefficients equal to 0.8934, 0.8376, and 0.8113, respectively. These results reached the state of the art in glioma segmentation determined by the winners of the challenge.Comment: 34 pages, 12 tables, 23 figure

Radiomic Features to Predict Overall Survival Time for Patients with Glioblastoma Brain Tumors Based on Machine Learning and Deep Learning Methods

Machine Learning (ML) methods including Deep Learning (DL) Methods have been employed in the medical field to improve diagnosis process and patient’s prognosis outcomes. Glioblastoma multiforme is an extremely aggressive Glioma brain tumor that has a poor survival rate. Understanding the behavior of the Glioblastoma brain tumor is still uncertain and some factors are still unrecognized. In fact, the tumor behavior is important to decide a proper treatment plan and to improve a patient’s health. The aim of this dissertation is to develop a Computer-Aided-Diagnosis system (CADiag) based on ML/DL methods to automatically estimate the Overall Survival Time (OST) for patients with Glioblastoma brain tumors from medical imaging and non-imaging data. This system is developed to enhance and speed-up the diagnosis process, as well as to increase understanding of the behavior of Glioblastoma brain tumors. The proposed OST prediction system is developed based on a classification process to categorize a GBM patient into one of the following three survival time groups: short-term (months), mid-term (10-15 months), and long-term (\u3e15 months). The Brain Tumor Segmentation challenge (BraTS) dataset is used to develop the automatic OST prediction system. This dataset consists of multimodal preoperative Magnetic Resonance Imaging (mpMRI) data, and clinical data. The training data is relatively small in size to train an accurate OST prediction model based on DL method. Therefore, traditional ML methods such as Support Vector Machine (SVM), Neural Network, K-Nearest Neighbor (KNN), Decision Tree (DT) were used to develop the OST prediction model for GBM patients. The main contributions in the perspective of ML field include: developing and evaluating five novel radiomic feature extraction methods to produce an automatic and reliable OST prediction system based on classification task. These methods are volumetric, shape, location, texture, histogram-based, and DL features. Some of these radiomic features can be extracted directly from MRI images, such as statistical texture features and histogram-based features. However, preprocessing methods are required to extract automatically other radiomic features from MRI images such as the volume, shape, and location information of the GBM brain tumors. Therefore, a three-dimension (3D) segmentation DL model based on modified U-Net architecture is developed to identify and localize the three glioma brain tumor subregions, peritumoral edematous/invaded tissue (ED), GD-enhancing tumor (ET), and the necrotic tumor core (NCR), in multi MRI scans. The segmentation results are used to calculate the volume, location and shape information of a GBM tumor. Two novel approaches based on volumetric, shape, and location information, are proposed and evaluated in this dissertation. To improve the performance of the OST prediction system, information fusion strategies based on data-fusion, features-fusion and decision-fusion are involved. The best prediction model was developed based on feature fusions and ensemble models using NN classifiers. The proposed OST prediction system achieved competitive results in the BraTS 2020 with accuracy 55.2% and 55.1% on the BraTS 2020 validation and test datasets, respectively. In sum, developing automatic CADiag systems based on robust features and ML methods, such as our developed OST prediction system, enhances the diagnosis process in terms of cost, accuracy, and time. Our OST prediction system was evaluated from the perspective of the ML field. In addition, preprocessing steps are essential to improve not only the quality of the features but also boost the performance of the prediction system. To test the effectiveness of our developed OST system in medical decisions, we suggest more evaluations from the perspective of biology and medical decisions, to be then involved in the diagnosis process as a fast, inexpensive and automatic diagnosis method. To improve the performance of our developed OST prediction system, we believe it is required to increase the size of the training data, involve multi-modal data, and/or provide any uncertain or missing information to the data (such as patients\u27 resection statuses, gender, etc.). The DL structure is able to extract numerous meaningful low-level and high-level radiomic features during the training process without any feature type nominations by researchers. We thus believe that DL methods could achieve better predictions than ML methods if large size and proper data is available

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.