Fetal growth restriction, affecting up to 10% of pregnancies, is a critical factor contributing to perinatal mortality and morbidity. Ultrasound measurements of the fetal abdominal circumference (AC) are a key aspect of monitoring fetal growth. However, the routine practice of biometric obstetric ultrasounds is limited in low-resource settings due to the high cost of sonography equipment and the scarcity of trained sonographers. To address this issue, we organized the ACOUSLIC-AI (Abdominal Circumference Operator-agnostic UltraSound measurement in Low-Income Countries) challenge to investigate the feasibility of automatically estimating fetal AC from blind-sweep ultrasound scans acquired by novice operators using low-cost devices. Training data, collected from three Public Health Units (PHUs) in Sierra Leone are made publicly available. Private validation and test sets, containing data from two PHUs in Tanzania and a European hospital, are provided through the Grand-Challenge platform. All sets were annotated by experienced readers. Sixteen international teams participated in this challenge, with six teams submitting to the Final Test Phase. In this article, we present the results of the three top-performing AI models from the ACOUSLIC-AI challenge, which are publicly accessible. We evaluate their performance in fetal abdomen frame selection, segmentation, abdominal circumference measurement, and compare their performance against clinical standards for fetal AC measurement. Clinical comparisons demonstrated that the limits of agreement (LoA) for A2 in fetal AC measurements are comparable to the interobserver LoA reported in the literature. The algorithms developed as part of the ACOUSLIC-AI challenge provide a benchmark for future algorithms on the selection and segmentation of fetal abdomen frames to further minimize fetal abdominal circumference measurement variability.

Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance.

An important limitation of state-of-the-art deep learning networks is that they do not recognize when their input is dissimilar to the data on which they were trained and proceed to produce outputs that will be unreliable or nonsensical. In this work, we describe FRODO (Free Rejection of Out-of-Distribution), a publicly available method that can be easily employed for any trained network to detect input data from a different distribution than is expected. FRODO uses the statistical distribution of intermediate layer outputs to define the expected in-distribution (ID) input image properties. New samples are judged based on the Mahalanobis distance (MD) of their layer outputs from the defined distribution. The method can be applied to any network, and we demonstrate the performance of FRODO in correctly rejecting OOD samples on three distinct architectures for classification, localization, and segmentation tasks in chest X-rays. A dataset of 21,576 X-ray images with 3,655 in-distribution samples is defined for testing. The remaining images are divided into four OOD categories of varying levels of difficulty, and performance at rejecting each type is evaluated using receiver operating characteristic (ROC) analysis. FRODO achieves areas under the ROC (AUC) of between 0.815 and 0.999 in distinguishing OOD samples of different types. This is shown to be comparable with the best-performing state-of-the-art method tested, with the substantial advantage that FRODO integrates seamlessly with any network and requires no extra model to be constructed and trained.

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs published before March 2021, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Detailed descriptions of all publicly available datasets are included and commercial systems in the field are described. A comprehensive discussion of the current state of the art is provided, including caveats on the use of public datasets, the requirements of clinically useful systems and gaps in the current literature.

An unsolved problem in medical image analysis is validation of methods. In this paper we will focus on image registration and in particular on nonlinear image registration, which is one of the hardest analysis problems to validate. The paper covers currently used methods of validation, comparative challenges and public datasets, as well as some of our own work in this area.

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.

Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.