Zobrazeno 1 - 10
of 6 025
pro vyhledávání: '"Automatic segmentation"'
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
Autor:
А. D. Nikitina
Publikováno v:
Вопросы лесной науки, Vol 7, Iss 2, Pp 1-21 (2024)
The article presents the results of applying an improved method for automatic segmentation of RGB imagery obtained using consumer-grade UAVs, based on the Mask R-CNN neural network architecture. Blocks for the preparation and post-processing of raste
Externí odkaz:
https://doaj.org/article/12634ae9b3cb467e91b4289ecc0be066
Autor:
Yao-Wen Liang, Yu-Ting Fang, Ting-Chun Lin, Cheng-Ru Yang, Chih-Chang Chang, Hsuan-Kan Chang, Chin-Chu Ko, Tsung-Hsi Tu, Li-Yu Fay, Jau-Ching Wu, Wen-Cheng Huang, Hsiang-Wei Hu, You-Yin Chen, Chao-Hung Kuo
Publikováno v:
Neurospine, Vol 21, Iss 2, Pp 665-675 (2024)
Objective This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are li
Externí odkaz:
https://doaj.org/article/d70fb37db0e349578dcd9ae6446eb72c
Autor:
Zhenyu Cheng, Linfeng Yang, Jing Li, Yiwen Chen, Pengcheng Liang, Yuanyuan Wang, Na Wang, Xinyue Zhang, Yian Gao, Chaofan Sui, Meng Li, Changhu Liang, Lingfei Guo
Publikováno v:
Neurobiology of Disease, Vol 202, Iss , Pp 106716- (2024)
Although the amygdala is associated with cognitive impairment resulting from cerebral small vessel disease, the relationship between alterations in amygdala structure and cerebral small vessel disease (CSVD) remains controversial. Given that the amyg
Externí odkaz:
https://doaj.org/article/7ea6172327864392a7d2432f51b5c6b9
Research trends and hotspots in fundus image segmentation from 2007 to 2023: A bibliometric analysis
Autor:
Hairui Deng, Yiren Wang, Venhui Cheng, Yongcheng He, Zhongjian Wen, Shouying Chen, Shengmin Guo, Ping Zhou, Yi Wang
Publikováno v:
Heliyon, Vol 10, Iss 21, Pp e39329- (2024)
Objective: To understand the current status, research hotspots, and trends of automatic segmentation of fundus lesion images worldwide, providing a reference for subsequent related studies. Methods: The electronic database Web of Science Core Collect
Externí odkaz:
https://doaj.org/article/17ee3c6ac4a14113a1775cd689db028a
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
ObjectiveThis study aims to develop and validate SwinHS, a deep learning-based automatic segmentation model designed for precise hippocampus delineation in patients receiving hippocampus-protected whole-brain radiotherapy. By streamlining this proces
Externí odkaz:
https://doaj.org/article/0f18ddb2f47f439089230eefca90b0fb
Autor:
Runyuan Wang, Xingcai Chen, Xiaoqin Zhang, Ping He, Jinfeng Ma, Huilin Cui, Ximei Cao, Yongjian Nian, Ximing Xu, Wei Wu, Yi Wu
Publikováno v:
Cancer Medicine, Vol 13, Iss 18, Pp n/a-n/a (2024)
Abstract Objective To create a deep‐learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structures using the UperNet Swin network and computed tomography angiography (CTA) images and to
Externí odkaz:
https://doaj.org/article/128807c0ec4d4b88bc39b356ced28a3b