Zobrazeno 1 - 10
of 1 003
pro vyhledávání: '"nuclei segmentation"'
Publikováno v:
Alexandria Engineering Journal, Vol 110, Iss , Pp 557-566 (2025)
Accurate nuclei segmentation is essential for extracting quantitative information from histology images to support disease diagnosis and treatment decisions. However, precise segmentation is challenging due to the presence of clustered nuclei, varied
Externí odkaz:
https://doaj.org/article/5932682b35c24d3ab205a54c17adc7b7
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 669-678 (2024)
With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological imag
Externí odkaz:
https://doaj.org/article/3560b1796cfa4b1e93039f111cfd902f
Publikováno v:
Human-Centric Intelligent Systems, Vol 4, Iss 3, Pp 417-436 (2024)
Abstract As the rise in cancer cases, there is an increasing demand to develop accurate and rapid diagnostic tools for early intervention. Pathologists are looking to augment manual analysis with computer-based evaluation to develop more efficient ca
Externí odkaz:
https://doaj.org/article/a38357d3ddb9431a8dd8eaa0ace7da4a
Publikováno v:
IEEE Access, Vol 12, Pp 107089-107097 (2024)
This research introduces an advanced approach to automate the segmentation and quantification of nuclei in fluorescent images through deep learning techniques. Overcoming inherent challenges such as variations in pixel intensities, noisy boundaries,
Externí odkaz:
https://doaj.org/article/a04ca5f1637443439766a60a9c1f9c10
Publikováno v:
Engineering Science and Technology, an International Journal, Vol 51, Iss , Pp 101636- (2024)
In spite of achieving human-level efficacy in gland and nuclei segmentation, modern deep learning-driven techniques still face challenges related to the loss of regional context information and disregard the importance of long-range semantic informat
Externí odkaz:
https://doaj.org/article/42fd19a35ac94e46a1d2f5b12e65b0da
Publikováno v:
BMC Medical Imaging, Vol 23, Iss 1, Pp 1-9 (2023)
Abstract Background The deterministic deep learning models have achieved state-of-the-art performance in various medical image analysis tasks, including nuclei segmentation from histopathology images. The deterministic models focus on improving the m
Externí odkaz:
https://doaj.org/article/33f7c19fa5ba49aa9df82cb75f3130fb
Publikováno v:
Information, Vol 15, Iss 7, p 417 (2024)
Separating overlapped nuclei is a significant challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on nuclei segmentation; however, their performance on separating overlapped nuclei is
Externí odkaz:
https://doaj.org/article/d8fc420bf26d46b0b46ffb2d671d9d1d
Autor:
Qicong Chen, Ming Cai, Xinjuan Fan, Wenbin Liu, Gang Fang, Su Yao, Yao Xu, Qian Li, Yingnan Zhao, Ke Zhao, Zaiyi Liu, Zhihua Chen
Publikováno v:
BMC Cancer, Vol 23, Iss 1, Pp 1-10 (2023)
Abstract Background and objective In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based o
Externí odkaz:
https://doaj.org/article/e70e85cae02b4806bff4bf556c80d7eb
Publikováno v:
Bioengineering, Vol 11, Iss 3, p 294 (2024)
Segmenting and classifying nuclei in H&E histopathology images is often limited by the long-tailed distribution of nuclei types. However, the strong generalization ability of image segmentation foundation models like the Segment Anything Model (SAM)
Externí odkaz:
https://doaj.org/article/26d4fe7d5fad41e38f0c51c999cf5283
Publikováno v:
Polish Journal of Pathology, Vol 73, Iss 2, Pp 134-158 (2022)
Externí odkaz:
https://doaj.org/article/610f210b4b7c42929a0bb9e9434ea3e8