Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network
IMT Atlantique（大西洋国立高等矿业电信学院）；LaTIM UMR 1101, Inserm（法国国家健康与医学研究院LaTIM UMR 1101实验室）；Symbiosis International University（共生国际大学）；Centre Hospitalier Régional et Universitaire (CHRU) de Brest（布雷斯特大学区域中心医院）
Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not perform well on scarce pediatric imaging data. We propose a new pre-trained regularized convolutional encoder-decoder network for the challenging task of segmenting heterogeneous pediatric magnetic resonance (MR) images. To this end, we have conceived a novel optimization scheme for the segmentation network which comprises additional regularization terms to the loss function. In order to obtain globally consistent predictions, we incorporate a shape priors based regularization, derived from a non-linear shape representation learnt by an auto-encoder. Additionally, an adversarial regularization computed by a discriminator is integrated to encourage precise delineations. The proposed method is evaluated for the task of multi-bone segmentation on two scarce pediatric imaging datasets from ankle and shoulder joints, comprising pathological as well as healthy examinations. The proposed method performed either better or at par with previously proposed approaches for Dice, sensitivity, specificity, maximum symmetric surface distance, average symmetric surface distance, and relative absolute volume difference metrics. We illustrate that the proposed approach can be easily integrated into various bone segmentation strategies and can improve the prediction accuracy of models pre-trained on large non-medical images databases. The obtained results bring new perspectives for the management of pediatric musculoskeletal disorders.
MRI-to-CT Synthesis With Cranial Suture Segmentations Using A Variational Autoencoder Framework
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, USA（Sheikh Zayed儿童外科创新研究所，Children’s National医院，华盛顿特区，美国）；CIBORG Lab, Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA（CIBORG实验室，放射科，Children’s Hospital Los Angeles，洛杉矶，加利福尼亚州，美国）；Departments of Radiology and Biomedical Engineering, University of Southern California, Los Angeles, CA, USA（放射科和生物医学工程系，美国南加州大学，洛杉矶，加利福尼亚州，美国）；School of Medicine and Health Sciences, George Washington University, Washington, DC, USA（医学与健康科学学院，乔治华盛顿大学，华盛顿特区，美国）
BoneAgeTW2: Automated Skeletal Maturation Assessment via the Tanner-Whitehouse 2 Method, Deep Learning, and Clinical Report Generation with Distribution Curves
Attend-and-Refine: Interactive keypoint estimation and quantitative cervical vertebrae analysis for bone age assessment
Kim Jaechul Graduate School of Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea（韩国科学技术院人工智能研究生院）；Korea University Anam Hospital, Seoul 02708, Republic of Korea（韩国大学安安医院）；Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea（釜山大学医学院_asan医院）
A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty
University of Science and Technology of China（中国科学技术大学）；The First Affiliated Hospital of the University of Science and Technology of China（中国科学技术大学附属第一医院）
From Data to Diagnosis: A Large, Comprehensive Bone Marrow Dataset and AI Methods for Childhood Leukemia Prediction
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）；University Hospital Erlangen（埃尔朗根大学医院）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；Friedrich-Alexander University of Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；PixelMed Publishing LLC（PixelMed出版有限责任公司）；Brigham and Women's Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔朗根-欧洲大都会区纽伦堡综合癌症中心）
Rapid Bone Scintigraphy Enhancement via Semantic Prior Distillation from Segment Anything Model
Department of Nuclear Medicine, Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University（核医学科，上海儿童医学中心，上海交通大学医学院）；School of Microelectronics, Shanghai University（微电子学院，上海大学）；Department of Ophthalmology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（眼科，瑞金医院，上海交通大学医学院）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；School of Software Engineering, Sun Yat-sen University（软件工程学院，中山大学）；College of Computer Science and Electronic Engineering, Hunan University（计算机科学与电子工程学院，湖南大学）；School of Computer Science and Technology, Henan Polytechnic University（计算机科学与技术学院，河南理工大学）
本文首次将 SAM 语义先验引入医学图像恢复，通过级联网络和三个语义模块增强儿科快速骨闪烁显像，并构建首个专用数据集 RBS，在多项指标上优于现有方法。
Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors
IMT Atlantique（大西洋国立高等矿业电信学校）；LaTIM UMR 1101, Inserm（法国国家健康与医学研究院LaTIM UMR 1101实验室）；Centre Hospitalier Régional et Universitaire (CHRU) de Brest（布雷斯特大学区域中心医院）；Fondation ILDYS（ILDYS基金会）；Symbiosis Center for Medical Image Analysis, Symbiosis International University（共生国际大学共生医学图像分析中心）
Multi-Structure Deep Segmentation with Shape Priors and Latent Adversarial Regularization
IMT Atlantique（IMT大西洋）；LaTIM UMR 1101, Inserm（LaTIM UMR 1101，法国国家健康与医学研究院）；Symbiosis Centre for Medical Image Analysis, Symbiosis International University（共生国际大学共生医学图像分析中心）；Fondation ILDYS（ILDYS基金会）
Towards Reliable Pediatric Brain Tumor Segmentation: Task-Specific nnU-Net Enhancements
Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC, Shanghai Jiao Tong University（自然科学院，数学科学学院，MOE-LSC，上海交通大学）；Department of Radiology, Huashan Hospital, Fudan University（放射科，华山医院，复旦大学）；Department of Neurosurgery, Huashan Hospital, Fudan University（神经外科，华山医院，复旦大学）
Scalable, Trustworthy Generative Model for Virtual Multi-Staining from H&E Whole Slide Images
Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Inria（法国国家信息与自动化研究所）；Paris Brain Institute-ICM（巴黎大脑研究所-脑研究所）；Paris Cité University（巴黎城市大学）；Necker-Enfants Malades University Hospitals（内克尔-儿童马尔代夫大学医院）；Centre de recherche sur l’inflammation（炎症研究中心）；Robert Debré University Hospitals（罗伯特德布雷大学医院）
