02834nam 2200541 450 991046105300332120200520144314.01-59756-807-4(CKB)3710000000452850(EBL)2050562(OCoLC)909145930(SSID)ssj0001481890(PQKBManifestationID)12562558(PQKBTitleCode)TC0001481890(PQKBWorkID)11523148(PQKB)10959967(MiAaPQ)EBC2050562(Au-PeEL)EBL2050562(CaPaEBR)ebr11056645(OCoLC)912320298(EXLCZ)99371000000045285020150601h20082008 uy 0engur|n|---|||||txtccrFalls assessment and prevention home, hospice, and extended care /Lynn S. AlvordSan Diego, California ;Oxford, [England] ;Brisbane, [Queensland] :Plural Publishing Inc.,2008.©20081 online resource (281 p.)Description based upon print version of record.1-59756-120-7 Includes bibliographical references and index.""Contents""; ""Preface""; ""Introduction""; ""Chapter 1. Scope of the Problemâ€?Hope for Solutions""; ""Chapter 2. Balance and Mobility""; ""Chapter 3. Why We Fall""; ""Chapter 4. Dizziness as a Symptom""; ""Chapter 5. The Falls Clinic and Examination""; ""Chapter 6. Preventing Falls""; ""Chapter 7. Falls Prevention in Hospitals and Nursing Facilities""; ""Chapter 8. Case Studies""; ""Appendix A. Suggested Additional Readings on Gait""; ""Appendix B. Example Falls Clinic Reports and Sample Blank Forms""; ""Appendix C. Safety Checklist""; ""Appendix D. Alvord Falls Screening Test""; ""Index""Among seniors, falls are the underlying cause of a large proportion of fatal traumatic brain injuries. On the positive side, much can be done to prevent injury from falls in the aging population. This book is an ideal guide for clinicians who see patients at risk for falling. It provides complete assessment and treatment plans, incorporating the most recent developments of new balance test equipment and techniques for balance function rehabilitation. The detailed section on assessment covers vestibular, cardiovascular, neurological, rheumatology, metabolic, orthopedic, psychological, cognitiveFalls (Accidents) in old ageElectronic books.Falls (Accidents) in old age.613.69Alvord Lynn Stephen1949-919020MiAaPQMiAaPQMiAaPQBOOK9910461053003321Falls assessment and prevention2061254UNINA04225nam 22005655 450 991072007070332120230504072653.03-031-31778-510.1007/978-3-031-31778-1(MiAaPQ)EBC7246023(Au-PeEL)EBL7246023(DE-He213)978-3-031-31778-1(OCoLC)1378743247(PPN)270612122(CKB)26581855700041(EXLCZ)992658185570004120230504d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierLeft Atrial and Scar Quantification and Segmentation First Challenge, LAScarQS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /edited by Xiahai Zhuang, Lei Li, Sihan Wang, Fuping Wu1st ed. 2023.Cham :Springer Nature Switzerland :Imprint: Springer,2023.1 online resource (174 pages)Lecture Notes in Computer Science,1611-3349 ;13586Print version: Zhuang, Xiahai Left Atrial and Scar Quantification and Segmentation Cham : Springer,c2023 9783031317774 LASSNet: A four steps deep neural network for Left Atrial Segmentation and Scar Quantification -- Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network -- Self Pre-training with Single-scale Adapter for Left Atrial Segmentation -- UGformer for Robust Left Atrium and Scar Segmentation Across Scanners -- Automatically Segmenting the Left Atrium and Scars from LGE-MRIs Using a boundary-focused nnU-Net -- Two Stage of Histogram Matching Augmentation for Domain Generalization : Application to Left Atrial Segmentation -- Sequential Segmentation of the Left Atrium and Atrial Scars Using a Multi-scale Weight Sharing Network and Boundary-based Processing -- LA-HRNet: High-resolution network for automatic left atrial segmentation in multi-center LEG MRI -- Edge-enhanced Features Guided Joint Segmentation and Quantification of Left Atrium and Scars in LGE MRI Images -- TESSLA: Two-Stage Ensemble Scar Segmentation for the Left Atrium -- Deep U-Net architecture with curriculum learning for left atrial segmentation -- Cross-domain Segmentation of Left Atrium Based on Multi-scale Decision Level Fusion -- Using Polynomial Loss and Uncertainty Information for Robust Left Atrial and Scar Quantification and Segmentation -- Automated segmentation of the left atrium and scar using deep convolutional neural networks -- Automatic Semi-Supervised Left Atrial Segmentation using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge.This book constitutes the First Left Atrial and Scar Quantification and Segmentation Challenge, LAScarQS 2022, which was held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, in Singapore, in September 2022. The 15 papers presented in this volume were carefully reviewed and selected form numerous submissions. The aim of the challenge is not only benchmarking various LA scar segmentation algorithms, but also covering the topic of general cardiac image segmentation, quantification, joint optimization, and model generalization, and raising discussions for further technical development and clinical deployment.Lecture Notes in Computer Science,1611-3349 ;13586Image processing—Digital techniquesComputer visionComputer Imaging, Vision, Pattern Recognition and GraphicsImage processing—Digital techniques.Computer vision.Computer Imaging, Vision, Pattern Recognition and Graphics.006616.120754Zhuang Xiahai1355673Li Lei1255050Wang Sihan1355674Wu Fuping1355675MiAaPQMiAaPQMiAaPQBOOK9910720070703321Left Atrial and Scar Quantification and Segmentation3359797UNINA