BGrowth: an efficient approach for the segmentation of vertebral compression fractures in magnetic resonance imaging
University of São Paulo（圣保罗大学）；University of Rondônia（朗多尼亚大学）
Jonathan S. Ramos, Carolina Y. V. Watanabe, Marcello H. Nogueira-Barbosa, Agma J. M. Traina
备注：
This is a pre-print of an article published in Symposium on Applied Computing. The final authenticated version is available online at https://doi.org/10.1145/3297280.3299728
发表信息：
The 34th ACM/SIGAPP Symposium on Applied Computing (SAC2019)
Segmentation of medical images is a critical issue: several process of analysis and classification rely on this segmentation. With the growing number of people presenting back pain and problems related to it, the automatic or semi-automatic segmentation of fractured vertebral bodies became a challenging task. In general, those fractures present several regions with non-homogeneous intensities and the dark regions are quite similar to the structures nearby. Aimed at overriding this challenge, in this paper we present a semi-automatic segmentation method, called Balanced Growth (BGrowth). The experimental results on a dataset with 102 crushed and 89 normal vertebrae show that our approach significantly outperforms well-known methods from the literature. We have achieved an accuracy up to 95% while keeping acceptable processing time performance, that is equivalent to the state-of-the-artmethods. Moreover, BGrowth presents the best results even with a rough (sloppy) manual annotation (seed points).
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning
HwaMei Hospital, University of Chinese Academy of Sciences (UCAS)（中国科学院大学华美医院）；Ningbo Institute of Life and Health Industry, UCAS（中国科学院大学宁波生命与健康产业研究院）；Harbin Institute of Technology（哈尔滨工业大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Technical University of Munich（慕尼黑工业大学）；Klinikum Rechts der Isar（伊萨尔河右岸医院）；Klinikum der Universität München（慕尼黑大学医院）；University of Munich（慕尼黑大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
HealthiVert-GAN: A Novel Framework of Pseudo-Healthy Vertebral Image Synthesis for Interpretable Compression Fracture Grading
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT)（先进磁共振诊断与治疗技术国家工程研究中心）；Med-X Research Institute, Shanghai Jiao Tong University（医-X研究院）；Kashi Prefecture Second People’s Hospital（喀什县第二人民医院）；Renji Hospital, Shanghai, China（仁济医院）；Shanghai Sixth People’s Hospital, Shanghai, China（上海第六人民医院）
Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization
IRA Labs Ltd（IRA实验室有限公司）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Kharkevich Institute for Information Transmission Problems（哈尔克维奇信息传输问题研究所）；Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department（莫斯科卫生部诊断与远程医疗技术研究实践临床中心）；Osimis SA（Osimis股份公司）；Artificial Intelligence Research Institute（人工智能研究所）
Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading
Technical University of Munich（慕尼黑工业大学）；Klinikum Rechts der Isar (Technical University of Munich)（慕尼黑工业大学伊萨尔河右岸医院）；University of Munich（慕尼黑大学）；Klinikum der Universität München（慕尼黑大学医院）；Johns Hopkins University（约翰斯·霍普金斯大学）
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling
University of Regensburg（雷根斯堡大学）；South Dakota State University（南达科他州立大学）；RWTH Aachen University（亚琛工业大学）；Northwestern University（西北大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）
Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Kharkevich Institute for Information Transmission Problems（哈尔克维奇信息传输问题研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies（诊断与远程医疗技术研究与实用临床中心）
Osteoporotic and Neoplastic Compression Fracture Classification on Longitudinal CT
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心放射与影像科学系影像生物标志物与计算机辅助诊断实验室）；Department of Radiological Sciences, University of California-Irvine, School of Medicine（加州大学尔湾分校医学院放射科学系）
AACLiteNet: A Lightweight Model for Detection of Fine-Grained Abdominal Aortic Calcification
Edith Cowan University（伊迪丝·考恩大学）；Nutrition and Health Innovation Research Institute（营养与健康创新研究所）；University of Manitoba（曼尼托巴大学）；Park Nicollet Clinic（帕尼克莱诊所）；HealthPartners Institute（健康伙伴研究所）；The University of Western Australia（西澳大学）
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（右侧伊萨尔医院）；Institute for Artificial Intelligence and Computer Science in Medicine, Technical University of Munich（慕尼黑工业大学人工智能与医学计算机科学研究所）；Computer Aided Medical Procedures, Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序研究所）；AI for Image-Guided Diagnosis and Therapy, Technical University of Munich（慕尼黑工业大学图像引导诊断与治疗人工智能研究所）
