Zobrazeno 1 - 10
of 6 229
pro vyhledávání: '"Automatic Segmentation"'
Autor:
Zaixian Zhang, Junqi Han, Weina Ji, Henan Lou, Zhiming Li, Yabin Hu, Mingjia Wang, Baozhu Qi, Shunli Liu
Publikováno v:
Journal of Medical Radiation Sciences, Vol 71, Iss 4, Pp 509-518 (2024)
Abstract Introduction The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentati
Externí odkaz:
https://doaj.org/article/9d9aa812b3da4113963cf6d73dd37c27
Publikováno v:
Thoracic Cancer, Vol 15, Iss 31, Pp 2235-2247 (2024)
Abstract Objective This study aimed to evaluate the feasibility and performance of deep transfer learning (DTL) networks with different types and dimensions in differentiating thymomas from thymic cysts in a retrospective cohort. Materials and Method
Externí odkaz:
https://doaj.org/article/15694095a7e345b0913ec8469abf752e
Autor:
Yukari Nagayasu, Shoki Inui, Yoshihiro Ueda, Akira Masaoka, Masahide Tominaga, Masayoshi Miyazaki, Koji Konishi
Publikováno v:
Journal of Medical Physics, Vol 49, Iss 3, Pp 335-342 (2024)
Aims: This study aimed to evaluate the geometrical accuracy of atlas-based auto-segmentation (ABAS), deformable image registration (DIR), and deep learning auto-segmentation (DLAS) in adaptive radiotherapy (ART) for head-and-neck cancer (HNC). Subjec
Externí odkaz:
https://doaj.org/article/c079bb8921084ac89b27fffd84cd53c9
Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi‐Center Study
Autor:
Xuetao Zhu, Dejian Liu, Lian Liu, Jingxuan Guo, Zedi Li, Yixiang Zhao, Tianhao Wu, Kaiwen Liu, Xinyu Liu, Xin Pan, Lei Qi, Yuanqiang Zhang, Lei Cheng, Bin Chen
Publikováno v:
Orthopaedic Surgery, Vol 16, Iss 8, Pp 2052-2065 (2024)
Background The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X‐ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicato
Externí odkaz:
https://doaj.org/article/8aebef88d5ba457983b1777a185b1169
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-13 (2024)
Abstract Schizophrenic patients’ brain tumor magnetic resonance imaging (MRI) images are important references for doctors to diagnose and treat schizophrenia. However, automatic segmentation of these images is a professional and tedious task. Exist
Externí odkaz:
https://doaj.org/article/97836a1b97844ee7854d9fb6b130dff7
Publikováno v:
Frontiers in Pediatrics, Vol 12 (2024)
IntroductionMonitoring the morphological features of the gestational sac (GS) and measuring the mean sac diameter (MSD) during early pregnancy are essential for predicting spontaneous miscarriage and estimating gestational age (GA). However, the manu
Externí odkaz:
https://doaj.org/article/d4af12fa5e4d47b89986158c59e61411
Autor:
Weimin Chen, Yong Han, Muhammad Awais Ashraf, Junhan Liu, Mu Zhang, Feng Su, Zhiguo Huang, Kelvin K.L. Wong
Publikováno v:
Journal of Bone Oncology, Vol 49, Iss , Pp 100649- (2024)
Background and objective: Magnetic resonance imaging (MRI) plays a vital role in diagnosing spinal diseases, including different types of spinal tumors. However, conventional segmentation techniques are often labor-intensive and susceptible to variab
Externí odkaz:
https://doaj.org/article/ee05d28ead1541a9a6ff55eb4e713da4
Autor:
Cyprien R. Fol, Nianfang Shi, Normand Overney, Arnadi Murtiyoso, Fabio Remondino, Verena C. Griess
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
While forest biodiversity faces a concerning decline, modern technology presents promising avenues for mitigation. However, a critical gap persists in reconciling ecological knowledge with the technical expertise required to use state-of-the-art tech
Externí odkaz:
https://doaj.org/article/7821ce1f2b6b414aa845a7699f23ce19
Publikováno v:
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol 12, Iss 1 (2024)
Patient-specific applications in biomechanics and orthopaedics call for segmentation of volumetric medical images. This task has historically been performed manually or semi-automatically, which entails significant effort by trained experts. Computer
Externí odkaz:
https://doaj.org/article/427a1622b47b433f98a1dee9d396712e
Autor:
Spyridon N. Karkavitsas, Marianne Göger-Neff, Maria Kawula, Kemal Sumser, Benjamin Zilles, Martin Wadepohl, Guillaume Landry, Christopher Kurz, Wolfgang G. Kunz, Olaf Dietrich, Lars H. Lindner, Margarethus M. Paulides
Publikováno v:
International Journal of Hyperthermia, Vol 41, Iss 1 (2024)
Introduction This study evaluated the performance of magnetic resonance thermometry (MRT) during deep-regional hyperthermia (HT) in pelvic and lower-extremity soft-tissue sarcomas.Materials and methods 17 pelvic (45 treatments) and 16 lower-extremity
Externí odkaz:
https://doaj.org/article/85a9921f4260473fa1a5c85f6e724e14