Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. However, the shape of the lungs is reported to be significantly different across the developmental stages from infancy to adulthood. This might result in age-related data domain shifts that would adversely impact lung segmentation performance when the models trained on the adult population are deployed for pediatric lung segmentation. In this work, our goal is to (i) analyze the generalizability of deep adult lung segmentation models to the pediatric population and (ii) improve performance through a stage-wise, systematic approach consisting of CXR modality-specific weight initializations, stacked ensembles, and an ensemble of stacked ensembles. To evaluate segmentation performance and generalizability, novel evaluation metrics consisting of mean lung contour distance (MLCD) and average hash score (AHS) are proposed in addition to the multi-scale structural similarity index measure (MS-SSIM), the intersection of union (IoU), Dice score, 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD). Our results showed a significant improvement (p < 0.05) in cross-domain generalization through our approach. This study could serve as a paradigm to analyze the cross-domain generalizability of deep segmentation models for other medical imaging modalities and applications.
VinBigData JSC（VinBigData 公司）；VinUniversity（温纳大学）；Phu Tho Department of Health（富寿省卫生厅）；Phu Tho General Hospital（富寿综合医院）
The development of diagnostic models for detecting and diagnosing pediatric diseases in CXR scans is undertaken due to the lack of high-quality physician-annotated datasets. To overcome this challenge, we introduce and release PediCXR, a new pediatric CXR dataset of 9,125 studies retrospectively collected from a major pediatric hospital in Vietnam between 2020 and 2021. Each scan was manually annotated by a pediatric radiologist with more than ten years of experience. The dataset was labeled for the presence of 36 critical findings and 15 diseases. In particular, each abnormal finding was iden…
Medical Imaging Center, Vingroup Big Data Institute（Vingroup大数据研究院医学影像中心）；College of Engineering & Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）
Chest radiograph (CXR) interpretation in pediatric patients is error-prone and requires a high level of understanding of radiologic expertise. Recently, deep convolutional neural networks (D-CNNs) have shown remarkable performance in interpreting CXR in adults. However, there is a lack of evidence indicating that D-CNNs can recognize accurately multiple lung pathologies from pediatric CXR scans. In particular, the development of diagnostic models for the detection of pediatric chest diseases faces significant challenges such as (i) lack of physician-annotated datasets and (ii) class imbalance …
University of São Paulo（圣保罗大学）；Instituto da Criança – HC-FM（儿童研究所——医学院附属医院）
The importance of imaging exams, such as Magnetic Resonance Imaging (MRI), for the diagnostic and follow-up of pediatric pathologies and the assessment of anatomical structures' development has been increasingly highlighted in recent times. Manual analysis of MRIs is time-consuming, subjective, and requires significant expertise. To mitigate this, automatic techniques are necessary. Most techniques focus on adult subjects, while pediatric MRI has specific challenges such as the ongoing anatomical and histological changes related to normal development of the organs, reduced signal-to-noise rati…
Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations
University Hospital RWTH Aachen（亚琛工业大学附属医院）；Hamedan University of Technology（哈马丹理工大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Stanford University（斯坦福大学）
Deep learning-based lung segmentation and automatic regional template in chest X-ray images for pediatric tuberculosis
Universidad Politécnica de Madrid（马德里理工大学）；CIBER-BBN（生物工程、生物材料与纳米医学网络研究中心）；Children’s National Hospital（国家儿童医院）；George Washington University School of Medicine（乔治华盛顿大学医学院）；ISGlobal（巴塞罗那全球健康研究所）；Hospital Clínic（巴塞罗那临床医院）；Universitat de Barcelona（巴塞罗那大学）；Stellenbosch University（斯泰伦博斯大学）
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System（儿童国家卫生系统谢赫扎耶德儿科外科创新研究所）；Children’s National Health System（儿童国家卫生系统）；San Francisco State University（旧金山州立大学）；School of Medicine and Health Sciences, George Washington University（乔治华盛顿大学医学与健康科学学院）
Computer-aided diagnosis (CAD) techniques for lung field segmentation from chest radiographs (CXR) have been proposed for adult cohorts, but rarely for pediatric subjects. Statistical shape models (SSMs), the workhorse of most state-of-the-art CXR-based lung field segmentation methods, do not efficiently accommodate shape variation of the lung field during the pediatric developmental stages. The main contributions of our work are: (1) a generic lung field segmentation framework from CXR accommodating large shape variation for adult and pediatric cohorts; (2) a deep representation learning dete…
The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；Vector Institute（矢量研究所）
Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms. However, it is a time-consuming process and requires neuroradiology expertise. While there has been extensive research focused on optimizing brain tumor segmentation in the adult population, studies on AI guided pediatric tumor segmentation are scarce. Furthermore, MRI signal characteristics of pediatric and adult brain tumors differ, necessitating the development of segmentation algorithms specifically designed for pediatric brain tumors. We developed a segmentation model trained on magnetic resonance…
PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning
Institut de cancérologie Strasbourg Europe (ICANS), Strasbourg, France（斯特拉斯堡欧洲癌症研究所）；ICube Laboratory, CNRS UMR‑7357, University of Strasbourg, France（ICube实验室，法国斯特拉斯堡大学）
DCRA-Net: Attention-Enabled Reconstruction Model for Dynamic Fetal Cardiac MRI
King’s College London（伦敦国王学院）；Evelina London Children’s Hospital（伊芙琳娜伦敦儿童医院）；Imperial College London（帝国理工学院）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）
Pediatric Pancreas Segmentation from MRI Scans with Deep Learning
Northwestern University（西北大学）；Gazi University Faculty of Medicine（加齐大学医学院）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Washington University School of Medicine in St. Louis（圣路易斯华盛顿大学医学院）；University of Iowa, Carver College of Medicine（爱荷华大学卡弗医学院）
In the past few years, deep learning (DL) models have drawn great attention and shown superior performance on brain tumor and subregion segmentation tasks. However, the success is limited to segmentation of adult gliomas, where sufficient data have been collected, manually labeled, and published for training DL models. It is still challenging to segment pediatric tumors, because the appearances are different from adult gliomas. Hence, directly applying a pretained DL model on pediatric data usually generates unacceptable results. Because pediatric data is very limited, both labeled and unlabel…
Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort
Department of Biomedical Engineering, Linköping University, Sweden（生物医学工程系，利厄普大学）；Center for Medical Image Science and Visualization, Linköping University, Sweden（医学影像科学与可视化中心，利厄普大学）；Crown Princess Victoria Children’s Hospital and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（维多利亚公主儿童医院和健康、医学与关怀科学系，利厄普大学）；Department of Radiology and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（放射科和健康、医学与关怀科学系，利厄普大学）；Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden（肿瘤病理学系，卡罗林斯卡研究所，索纳）；Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Sweden（临床病理学和癌症诊断系，卡罗林斯卡大学医院）；Department of Radiation Physics and Department of Medical and Health Sciences, Linköping University, Sweden（放射物理系和医学与健康科学系，利厄普大学）
A New Logic For Pediatric Brain Tumor Segmentation
1 Northwestern University, Feinberg School of Medicine, Chicago IL, USA 2 Children’s National Hospital, Sheikh Zayed Institute for Pediatric Surgical Innovation, Washington, DC, USA 3 Ann \& Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA
Vingroup Big Data Institute（Vingroup大数据研究院）；VinUniversity（VinUniversity大学）
X-ray imaging in DICOM format is the most commonly used imaging modality in clinical practice, resulting in vast, non-normalized databases. This leads to an obstacle in deploying AI solutions for analyzing medical images, which often requires identifying the right body part before feeding the image into a specified AI model. This challenge raises the need for an automated and efficient approach to classifying body parts from X-ray scans. Unfortunately, to the best of our knowledge, there is no open tool or framework for this task to date. To fill this lack, we introduce a DICOM Imaging Router …
A Multi-Stage Deep Learning Framework with PKCP-MixUp Augmentation for Pediatric Liver Tumor Diagnosis Using Multi-Phase Contrast-Enhanced CT
Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院武汉儿童医院（武汉市妇幼保健院））；Wuhan University（武汉大学）；Wuhan Cloud Computing Technology Co., Ltd.（武汉云计算技术有限公司）
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Sagol Brain Institute, Tel Aviv Medical Center（特拉维夫医疗中心萨戈尔脑研究所）；Tel Aviv Medical Center（特拉维夫医疗中心）；Tel Aviv University（特拉维夫大学）；Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心）
Normal fetal adipose tissue (AT) development is essential for perinatal well-being. AT, or simply fat, stores energy in the form of lipids. Malnourishment may result in excessive or depleted adiposity. Although previous studies showed a correlation between the amount of AT and perinatal outcome, prenatal assessment of AT is limited by lacking quantitative methods. Using magnetic resonance imaging (MRI), 3D fat- and water-only images of the entire fetus can be obtained from two point Dixon images to enable AT lipid quantification. This paper is the first to present a methodology for developing …
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia（费城儿童医院生物医学数据驱动发现中心）；University of Pennsylvania（宾夕法尼亚大学）；Center for AI & Data Science for Integrated Diagnostics (AI2D)（综合诊断人工智能与数据科学中心）；Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania（宾夕法尼亚大学生物医学图像计算与分析中心）；Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital（儿童医院谢赫·扎耶德儿科外科创新研究所）；Thomas Jefferson University Hospital（托马斯杰斐逊大学医院）；Sage Bionetworks；Montreal Neurological Institute (MNI), McGill University（麦吉尔大学蒙特利尔神经学研究所）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University of Southern California（南加州大学）；Duke University Medical Center（杜克大学医学中心）；Mayo Clinic（梅奥诊所）；Center for Global Health, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院全球健康中心）；Technical University of Munich（慕尼黑工业大学）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学转化肿瘤学研究中心（TranslaTUM））；National Cancer Institute（美国国家癌症 Institute）
Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble
Sheikh Zayed Institute for Pediatric Surgical Innovation（Sheikh Zayed儿童外科创新研究所）；Children’s National Hospital（儿童医院）；University of Washington（华盛顿大学）；Universidad Politécnica de Madrid（马德里理工大学）；CIBER-BBN；ISCIII；School of Medicine and Health Sciences（医学与健康科学学院）
