Zobrazeno 1 - 10
of 2 425
pro vyhledávání: '"brain tumor segmentation"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract Glioma refers to a highly prevalent type of brain tumor that is strongly associated with a high mortality rate. During the treatment process of the disease, it is particularly important to accurately perform segmentation of the glioma from M
Externí odkaz:
https://doaj.org/article/9cce66bba0b6498fa4e8e027303b1c5d
Publikováno v:
Tomography, Vol 10, Iss 10, Pp 1577-1590 (2024)
Background: Existing methods for MRI brain tumor segmentation often suffer from excessive model parameters and suboptimal performance in delineating tumor boundaries. Methods: For this issue, a lightweight MRI brain tumor segmentation method, enhance
Externí odkaz:
https://doaj.org/article/a1439acca8684f4c95afd549b07df0f3
Autor:
AmirReza BabaAhmadi, Zahra FallahPour
Publikováno v:
AUT Journal of Mathematics and Computing, Vol 6, Iss 1, Pp 19-30 (2025)
This study explores the use of efficient deep learning algorithms for segmenting lower grade gliomas (LGG) in medical images. It evaluates various pre-trained atrous-convolutional architectures and U-Nets, proposing a novel transformer-based approach
Externí odkaz:
https://doaj.org/article/cfea788147344438a81b338d597d3962
Publikováno v:
Radioengineering, Vol 33, Iss 3, Pp 387-396 (2024)
Brain tumors refer to abnormal cell proliferation formed in brain tissue, which can cause neurological dysfunction and cognitive impairment, posing a serious threat to human health. Therefore, it becomes a very challenging work to full-automaticly se
Externí odkaz:
https://doaj.org/article/8d9353f371a84403957c4824df3fb3e7
Autor:
Niha Kamal Basha, Christo Ananth, K. Muthukumaran, Gadug Sudhamsu, Vikas Mittal, Fikreselam Gared
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)
Abstract The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convoluti
Externí odkaz:
https://doaj.org/article/c562bad41cc144a0982c996f11f259f2
Autor:
Abdulkhalek Al-Fakih, Abdullah Shazly, Abbas Mohammed, Mohammed Elbushnaq, Kanghyun Ryu, Yeong Hyeon Gu, Mohammed A. Al-masni, Meena M. Makary
Publikováno v:
Alexandria Engineering Journal, Vol 99, Iss , Pp 108-123 (2024)
Manual segmentation of brain tumors using structural magnetic resonance imaging (MRI) is an arduous and time-consuming task. Therefore, automatic and robust segmentation will considerably influence neuro-oncological clinical trials by reducing excess
Externí odkaz:
https://doaj.org/article/38ed10efd89e447a839c94e7571a54c1
Publikováno v:
Frontiers in Oncology, Vol 14 (2024)
IntroductionThis study presented an end-to-end 3D deep learning model for the automatic segmentation of brain tumors.MethodsThe MRI data used in this study were obtained from a cohort of 630 GBM patients from the University of Pennsylvania Health Sys
Externí odkaz:
https://doaj.org/article/6d53392e3e6d4caa9575ce710dfa0a26
Publikováno v:
IET Image Processing, Vol 18, Iss 7, Pp 1809-1822 (2024)
Abstract Brain tumour segmentation (BTS) is crucial for diagnosis and treatment planning by delineating tumour boundaries and subregions in multi‐modality bio‐imaging data. Several BTS models have been proposed to address specific technical chall
Externí odkaz:
https://doaj.org/article/a34b4b26d4264156bd486c32fe8114f5
Publikováno v:
Heliyon, Vol 10, Iss 18, Pp e37804- (2024)
Brain tumors are one of the leading causes of cancer death; screening early is the best strategy to diagnose and treat brain tumors. Magnetic Resonance Imaging (MRI) is extensively utilized for brain tumor diagnosis; nevertheless, achieving improved
Externí odkaz:
https://doaj.org/article/3362e861aed64e24bc8acd1abf6433fc
Publikováno v:
Egyptian Informatics Journal, Vol 27, Iss , Pp 100528- (2024)
Accurate brain tumor segmentation in MRI images is crucial for effective treatment planning and monitoring. Traditional methods often encounter challenges due to the complexity and variability of tumor shapes and textures. Consequently, there is a gr
Externí odkaz:
https://doaj.org/article/76434466164e4eb98237dc4d618f3b70