Metrics reloaded: recommendations for image analysis validation.

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers.

Virtual reality in the management of stress and anxiety disorders: A retrospective analysis of 61 people treated in the metaverse.

Mental health is the second largest group of health disorders associated with prolonged disability. Treating conditions such as stress and anxiety are a global health challenge due to inadequate funding and resources. Therefore, providing virtual treatment in the metaverse may provide a novel method of treatment for these conditions.

Virtual reality in the management of patients with low back and neck pain: a retrospective analysis of 82 people treated solely in the metaverse.

Background: Clinically, neck pain disorders (NPD) and non-specific low back pain (NS-LBP) are respectively the fourth and first most common conditions associated with the greatest number of years lived with disability. Remote delivery of care may benefit healthcare sustainability, reduce environmental pollution, and free up space for those requiring care non-virtual care.

Methods: A retrospective analysis was performed on 82 participants with NS-LBP and/or NPD who received exercise therapy delivered solely in the metaverse using virtually reality.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers.

Balancing new technology: Virtual reality for balance measurement case report.

Rationale: Falling and the inability to maintain balance are the second leading cause of unintentional injury deaths globally. There are a number of chronic and acute conditions characterized by balance difficulties, including neurological diseases, and sport injuries. Therefore, methods to monitor and quantify balance are critical for clinical decision-making regarding risk management and balance rehabilitation.

Introduction: Medical Mobilities in the Modern Middle East and North Africa.

This themed section contributes to efforts to conceptualize medical mobility. It does so by observing medical histories within the Middle East while following concrete movements. This focus on what moves and how, rather than on largely static and fixed units of analysis on where to, is central to the studies in this issue.

Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning.

Disability progression in multiple sclerosis remains resistant to treatment. The absence of a suitable biomarker to allow for phase 2 clinical trials presents a high barrier for drug development. We propose to enable short proof-of-concept trials by increasing statistical power using a deep-learning predictive enrichment strategy.

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference.

Although deep networks have been shown to perform very well on a variety of medical imaging tasks, inference in the presence of pathology presents several challenges to common models. These challenges impede the integration of deep learning models into real clinical workflows, where the customary process of cascading deterministic outputs from a sequence of image-based inference steps (e.g.

BIAS: Transparent reporting of biomedical image analysis challenges.

The number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common data sets, typically to identify the best method for a given problem. Recent research, however, revealed that common practice related to challenge reporting does not allow for adequate interpretation and reproducibility of results.

Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis.

The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients.

Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation.

Deep learning networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets, particularly on metrics focused on large pathologies. For diseases such as Multiple Sclerosis (MS), however, monitoring all the focal lesions visible on MRI sequences, even very small ones, is essential for disease staging, prognosis, and evaluating treatment efficacy. Small lesion segmentation presents significant challenges to popular deep learning models.

Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields.

Robust and reliable stroke lesion segmentation is a crucial step toward employing lesion volume as an independent endpoint for randomized trials. The aim of this work was to develop and evaluate a novel method to segment sub-acute ischemic stroke lesions from fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) datasets. After preprocessing of the datasets, a Bayesian technique based on Gabor textures extracted from the FLAIR signal intensities is utilized to generate a first estimate of the lesion segmentation.

Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care.

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

Why rankings of biomedical image analysis competitions should be interpreted with care.

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now.

Combining intraoperative ultrasound brain shift correction and augmented reality visualizations: a pilot study of eight cases.

We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient's cortex, vasculature, and lesion.

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.

IBIS: an OR ready open-source platform for image-guided neurosurgery.

Purpose: Navigation systems commonly used in neurosurgery suffer from two main drawbacks: (1) their accuracy degrades over the course of the operation and (2) they require the surgeon to mentally map images from the monitor to the patient. In this paper, we introduce the Intraoperative Brain Imaging System (IBIS), an open-source image-guided neurosurgery research platform that implements a novel workflow where navigation accuracy is improved using tracked intraoperative ultrasound (iUS) and the visualization of navigation information is facilitated through the use of augmented reality (AR).

Methods: The IBIS platform allows a surgeon to capture tracked iUS images and use them to automatically update preoperative patient models and plans through fast GPU-based reconstruction and registration methods.

Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images.

Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms.

Hierarchical Spatio-Temporal Probabilistic Graphical Model with Multiple Feature Fusion for Binary Facial Attribute Classification in Real-World Face Videos.

Recent literature shows that facial attributes, i.e., contextual facial information, can be beneficial for improving the performance of real-world applications, such as face verification, face recognition, and image search.

IMaGe: Iterative Multilevel Probabilistic Graphical Model for Detection and Segmentation of Multiple Sclerosis Lesions in Brain MRI.

In this paper, we present IMaGe, a new, iterative two-stage probabilistic graphical model for detection and segmentation of Multiple Sclerosis (MS) lesions. Our model includes two levels of Markov Random Fields (MRFs). At the bottom level, a regular grid voxel-based MRF identifies potential lesion voxels, as well as other tissue classes, using local and neighbourhood intensities and class priors.

Adaptive multi-level conditional random fields for detection and segmentation of small enhanced pathology in medical images.

Detection and segmentation of large structures in an image or within a region of interest have received great attention in the medical image processing domains. However, the problem of small pathology detection and segmentation still remains an unresolved challenge due to the small size of these pathologies, their low contrast and variable position, shape and texture. In many contexts, early detection of these pathologies is critical in diagnosis and assessing the outcome of treatment.

Probabilistic multiple sclerosis lesion classification based on modeling regional intensity variability and local neighborhood information.

Goal: In this paper, a fully automatic probabilistic method for multiple sclerosis (MS) lesion classification is presented, whereby the posterior probability density function over healthy tissues and two types of lesions (T1-hypointense and T2-hyperintense) is generated at every voxel.

Methods: During training, the system explicitly models the spatial variability of the intensity distributions throughout the brain by first segmenting it into distinct anatomical regions and then building regional likelihood distributions for each tissue class based on multimodal magnetic resonance image (MRI) intensities. Local class smoothness is ensured by incorporating neighboring voxel information in the prior probability through Markov random fields.

Temporal Hierarchical Adaptive Texture CRF for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI.

We propose a conditional random field (CRF) based classifier for segmentation of small enhanced pathologies. Specifically, we develop a temporal hierarchical adaptive texture CRF (THAT-CRF) and apply it to the challenging problem of gad enhancing lesion segmentation in brain MRI of patients with multiple sclerosis. In this context, the presence of many nonlesion enhancements (such as blood vessels) renders the problem more difficult.