The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig

A CAD system for early diagnosis of autism using different imaging modalities.

The term “autism spectrum disorder” (ASD) refers to a collection of neuro-developmental disorders that affect linguistic, behavioral, and social skills. Autism has many symptoms, most prominently, social impairment and repetitive behaviors. It is crucial to diagnose autism at an early stage for better assessment and investigation of this complex syndrome. There have been a lot of efforts to diagnose ASD using different techniques, such as imaging modalities, genetic techniques, and behavior reports. Imaging modalities have been extensively exploited for ASD diagnosis, and one of the most successful ones is Magnetic resonance imaging(MRI),where it has shown promise for the early diagnosis of the ASD related abnormalities in particular. Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. After the advent in the nineteen eighties, MRI soon became one of the most promising non- invasive modalities for visualization and diagnostics of ASD-related abnormalities. Along with its main advantage of no exposure to radiation, high contrast, and spatial resolution, the recent advances to MRI modalities have notably increased diagnostic certainty. Multiple MRI modalities, such as different types of structural MRI (sMRI) that examines anatomical changes, and functional MRI (fMRI) that examines brain activity by monitoring blood flow changes,have been employed to investigate facets of ASD in order to better understand this complex syndrome. This work aims at developing a new computer-aided diagnostic (CAD) system for autism diagnosis using different imaging modalities. It mainly relies on making use of structural magnetic resonance images for extracting notable shape features from parts of the brainthat proved to correlate with ASD from previous neuropathological studies. Shape features from both the cerebral cortex (Cx) and cerebral white matter(CWM)are extracted. Fusion of features from these two structures is conducted based on the recent findings suggesting that Cx changes in autism are related to CWM abnormalities. Also, when fusing features from more than one structure, this would increase the robustness of the CAD system. Moreover, fMRI experiments are done and analyzed to find areas of activation in the brains of autistic and typically developing individuals that are related to a specific task. All sMRI findings are fused with those of fMRI to better understand ASD in terms of both anatomy and functionality,and thus better classify the two groups. This is one aspect of the novelty of this CAD system, where sMRI and fMRI studies are both applied on subjects from different ages to diagnose ASD. In order to build such a CAD system, three main blocks are required. First, 3D brain segmentation is applied using a novel hybrid model that combines shape, intensity, and spatial information. Second, shape features from both Cx and CWM are extracted and anf MRI reward experiment is conducted from which areas of activation that are related to the task of this experiment are identified. Those features were extracted from local areas of the brain to provide an accurate analysis of ASD and correlate it with certain anatomical areas. Third and last, fusion of all the extracted features is done using a deep-fusion classification network to perform classification and obtain the diagnosis report. Fusing features from all modalities achieved a classification accuracy of 94.7%, which emphasizes the significance of combining structures/modalities for ASD diagnosis. To conclude, this work could pave the pathway for better understanding of the autism spectrum by finding local areas that correlate to the disease. The idea of personalized medicine is emphasized in this work, where the proposed CAD system holds the promise to resolve autism endophenotypes and help clinicians deliver personalized treatment to individuals affected with this complex syndrome

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.Comment: Paper under revie

SPNet: Shape Prediction Using a Fully Convolutional Neural Network

This is the author accepted manuscript. The final version is available from Springer via the DOI in this recordShape has widely been used in medical image segmentation algorithms to constrain a segmented region to a class of learned shapes. Recent methods for object segmentation mostly use deep learning algorithms. The state-of-the-art deep segmentation networks are trained with loss functions defined in a pixel-wise manner, which is not suitable for learning topological shape information and constraining segmentation results. In this paper, we propose a novel shape predictor network for object segmentation. The proposed deep fully convolutional neural network learns to predict shapes instead of learning pixel-wise classification. We apply the novel shape predictor network to X-ray images of cervical vertebra where shape is of utmost importance. The proposed network is trained with a novel loss function that computes the error in the shape domain. Experimental results demonstrate the effectiveness of the proposed method to achieve state-of-the-art segmentation, with correct topology and accurate fitting that matches expert segmentation.Engineering and Physical Sciences Research Council (EPSRC)Royal Devon and Exeter NHS Foundation Trus

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.

Brain MR Image Segmentation: From Multi-Atlas Method To Deep Learning Models

Quantitative analysis of the brain structures on magnetic resonance (MR) images plays a crucial role in examining brain development and abnormality, as well as in aiding the treatment planning. Although manual delineation is commonly considered as the gold standard, it suffers from the shortcomings in terms of low efficiency and inter-rater variability. Therefore, developing automatic anatomical segmentation of human brain is of importance in providing a tool for quantitative analysis (e.g., volume measurement, shape analysis, cortical surface mapping). Despite a large number of existing techniques, the automatic segmentation of brain MR images remains a challenging task due to the complexity of the brain anatomical structures and the great inter- and intra-individual variability among these anatomical structures. To address the existing challenges, four methods are proposed in this thesis. The first work proposes a novel label fusion scheme for the multi-atlas segmentation. A two-stage majority voting scheme is developed to address the over-segmentation problem in the hippocampus segmentation of brain MR images. The second work of the thesis develops a supervoxel graphical model for the whole brain segmentation, in order to relieve the dependencies on complicated pairwise registration for the multi-atlas segmentation methods. Based on the assumption that pixels within a supervoxel are supposed to have the same label, the proposed method converts the voxel labeling problem to a supervoxel labeling problem which is solved by a maximum-a-posteriori (MAP) inference in Markov random field (MRF) defined on supervoxels. The third work incorporates attention mechanism into convolutional neural networks (CNN), aiming at learning the spatial dependencies between the shallow layers and the deep layers in CNN and producing an aggregation of the attended local feature and high-level features to obtain more precise segmentation results. The fourth method takes advantage of the success of CNN in computer vision, combines the strength of the graphical model with CNN, and integrates them into an end-to-end training network. The proposed methods are evaluated on public MR image datasets, such as MICCAI2012, LPBA40, and IBSR. Extensive experiments demonstrate the effectiveness and superior performance of the three proposed methods compared with the other state-of-the-art methods