White Matter Injury (WMI) is the most prevalent brain injury in the preterm neonate leading to developmental deficits. However, detecting WMI in Magnetic Resonance (MR) images of preterm neonate brains using traditional WM segmentation-based methods is difficult mainly due to lack of reliable preterm neonate brain atlases to guide segmentation. Hence, we propose a segmentation-free, fast, unsupervised, atlas-free WMI detection method. We detect the ventricles as blobs using a fast linear Maximally Stable Extremal Regions algorithm. A reference contour equidistant from the blobs and the brain-background boundary is used to identify tissue adjacent to the blobs. Assuming normal distribution of the gray-value intensity of this tissue, the outlier intensities in the entire brain region are identified as potential WMI candidates. Thereafter, false positives are discriminated using appropriate heuristics. Experiments using an expert-annotated dataset show that the proposed method runs 20 times faster than our earlier work which relied on time-consuming segmentation of the WM region, without compromising WMI detection accuracy.
Università Politecnica delle Marche（马尔凯理工大学）；Istituto Italiano di Tecnologia（意大利技术研究院）；University Hospital Ancona（安科纳大学医院）
Objective: Preterm infants' limb monitoring in neonatal intensive care units (NICUs) is of primary importance for assessing infants' health status and motor/cognitive development. Herein, we propose a new approach to preterm infants' limb pose estimation that features spatio-temporal information to detect and track limb joints from depth videos with high reliability. Methods: Limb-pose estimation is performed using a deep-learning framework consisting of a detection and a regression convolutional neural network (CNN) for rough and precise joint localization, respectively. The CNNs are implemen…
Deep learning-based neurodevelopmental assessment in preterm infants
Image Computing Laboratory（图像计算实验室）；Shaanxi University of Science and Technology（陕西科技大学）；Department of Neonatology（新生儿科）；Shenzhen University of Advanced Technology General Hospital（深圳先进技术大学附属医院）；Department of Neurosurgery（神经外科）；The First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）；Department of Radiotherapy（放射治疗科）；University of Groningen, University Medical Center Groningen（格罗宁根大学，格罗宁根大学医学中心）；CSIRO Data61
Universitá Politecnica delle Marche（马尔凯理工大学）；Istituto Italiano di Tencologia（意大利技术研究院）
Preterm infants' limb-pose estimation is a crucial but challenging task, which may improve patients' care and facilitate clinicians in infant's movements monitoring. Work in the literature either provides approaches to whole-body segmentation and tracking, which, however, has poor clinical value, or retrieve a posteriori limb pose from limb segmentation, increasing computational costs and introducing inaccuracy sources. In this paper, we address the problem of limb-pose estimation under a different point of view. We proposed a 2D fully-convolutional neural network for roughly detecting limb jo…
GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction
Image Computing Laboratory, Shaanxi University of Science and Technology（陕西科技大学图像计算实验室）；Department of Neonatology, Shenzhen University of Advanced Technology General Hospital（深圳先进技术医院新生儿科）；Department of Neurosurgery, The First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院神经外科）；CSIRO Technology（澳大利亚CSIRO技术）
This paper presents a deep learning framework for image classification aimed at increasing predictive performance for Cytotoxic Edema (CE) diagnosis in infants and children. The proposed framework includes two 3D network architectures optimized to learn from two types of clinical MRI data , a trace Diffusion Weighted Image (DWI) and the calculated Apparent Diffusion Coefficient map (ADC). This work proposes a robust and novel solution based on volumetric analysis of 3D images (using pixels from time slices) and 3D convolutional neural network (CNN) models. While simple in architecture, the pro…
Xidian University（西安电子科技大学）；The First Affiliated Hospital of Xi'an Jiaotong University（西安交通大学第一附属医院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Accurate segmentation of punctate white matter lesion (PWML) in infantile brains by an automatic algorithm can reduce the potential risk of postnatal development. How to segment PWML effectively has become one of the active topics in medical image segmentation in recent years. In this paper, we construct an efficient two-stage PWML semantic segmentation network based on the characteristics of the lesion, called refined segmentation R-CNN (RS RCNN). We propose a heuristic RPN (H-RPN) which can utilize surrounding information around the PWMLs for heuristic segmentation. Also, we design a lightwe…
The Blind Spot in 2D Infants' Pose Estimation:Robust Learning from Noisy Annotations
Università degli Studi “G. d’Annunzio” Chieti-Pescara（G. d'Annunzio 基耶蒂-佩斯卡拉大学）；University of Macerata（马切拉塔大学）；KU Leuven（鲁汶大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Università di Teramo（泰拉莫大学）
Site-Level Fine-Tuning with Progressive Layer Freezing: Towards Robust Prediction of Bronchopulmonary Dysplasia from Day-1 Chest Radiographs in Extremely Preterm Infants
Saarland University（萨尔兰大学）；University Hospital of Saarland（萨尔兰大学医院）；Clinical Centre Karlsruhe（卡尔斯鲁厄临床中心）；Helmholtz Institute for Pharmaceutical Research Saarland（亥姆霍兹萨尔兰药物研究所）；University Hospital of Augsburg（奥格斯堡大学医院）
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the informatio…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Wilhelmina Children’s Hospital, University Medical Center Utrecht（乌得勒支大学医学中心威廉明娜儿童医院）；Brain Center Rudolf Magnus, University Medical Center Utrecht（乌得勒支大学医学中心鲁道夫·马格努斯脑中心）
Automatic neonatal brain tissue segmentation in preterm born infants is a prerequisite for evaluation of brain development. However, automatic segmentation is often hampered by motion artifacts caused by infant head movements during image acquisition. Methods have been developed to remove or minimize these artifacts during image reconstruction using frequency domain data. However, frequency domain data might not always be available. Hence, in this study we propose a method for removing motion artifacts from the already reconstructed MR scans. The method employs a generative adversarial network…
Complementary Roles of Image Classification and Vessel Segmentation in AI-Based Screening for Retinopathy of Prematurity Plus Disease in a Kenyan Preterm Cohort
