Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Cefa Karabağ"'
Publikováno v:
Journal of Imaging, Vol 9, Iss 3, p 59 (2023)
This paper investigates the impact of the amount of training data and the shape variability on the segmentation provided by the deep learning architecture U-Net. Further, the correctness of ground truth (GT) was also evaluated. The input data consist
Externí odkaz:
https://doaj.org/article/25ca49af64f44e14a19007ed7771f83d
Autor:
Cefa Karabağ, Martin L Jones, Christopher J Peddie, Anne E Weston, Lucy M Collinson, Constantino Carlos Reyes-Aldasoro
Publikováno v:
PLoS ONE, Vol 15, Iss 10, p e0230605 (2020)
The quantitative study of cell morphology is of great importance as the structure and condition of cells and their structures can be related to conditions of health or disease. The first step towards that, is the accurate segmentation of cell structu
Externí odkaz:
https://doaj.org/article/174a34d3ecd647db8fc7dbc9ca0a2222
Autor:
Ananda Ananda, Kwun Ho Ngan, Cefa Karabağ, Aram Ter-Sarkisov, Eduardo Alonso, Constantino Carlos Reyes-Aldasoro
Publikováno v:
Sensors, Vol 21, Iss 16, p 5381 (2021)
This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception
Externí odkaz:
https://doaj.org/article/6df7b90eb6ac40e49d6416a4a2cf6bef
Publikováno v:
Journal of Imaging, Vol 7, Iss 6, p 93 (2021)
In this work, an unsupervised volumetric semantic instance segmentation of the plasma membrane of HeLa cells as observed with serial block face scanning electron microscopy is described. The resin background of the images was segmented at different s
Externí odkaz:
https://doaj.org/article/b3db7f72d4e3415496ab2f92a8a92ac3
Autor:
Mauricio Alberto Ortega-Ruiz, Cefa Karabağ, Victor García Garduño, Constantino Carlos Reyes-Aldasoro
Publikováno v:
Journal of Imaging, Vol 6, Iss 10, p 101 (2020)
This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives informatio
Externí odkaz:
https://doaj.org/article/c0bcd4bbf7c94c589617fc6d1f32cf94
Publikováno v:
Applied Sciences, Vol 9, Iss 18, p 3900 (2019)
This paper compares a series of traditional and deep learning methodologies for the segmentation of textures. Six well-known texture composites first published by Randen and Husøy were used to compare traditional segmentation techniques (co-occurren
Externí odkaz:
https://doaj.org/article/7fb8c3e4aff84e118f912e76012bdce6
Autor:
Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro
Publikováno v:
Journal of Imaging, Vol 5, Iss 9, p 75 (2019)
This paper describes an unsupervised algorithm, which segments the nuclear envelope of HeLa cells imaged by Serial Block Face Scanning Electron Microscopy. The algorithm exploits the variations of pixel intensity in different cellular regions by calc
Externí odkaz:
https://doaj.org/article/cd6fbdc62c004131aa4cf3b83eccbcbb
Publikováno v:
Journal of Imaging
Volume 9
Issue 3
Pages: 59
Volume 9
Issue 3
Pages: 59
This paper investigates the impact of the amount of training data and the shape variability on the segmentation provided by the deep learning architecture U-Net. Further, the correctness of ground truth (GT) was also evaluated. The input data consist
This paper describes the quantitative and qualitative evaluation of training data, ground truth and the variability of the shape of HeLa cells as observed with electron microscopy in the process of semantic segmentation. HeLa cells are a widely used
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bf302fe314c11dc861f357cbceea3be5
https://doi.org/10.1101/2022.06.01.494316
https://doi.org/10.1101/2022.06.01.494316
Autor:
Mauricio Alberto Ortega-Ruiz, Cefa Karabag, Edgar Roman-Rangel, Constantino Carlos Reyes-Aldasoro
Publikováno v:
IEEE Access, Vol 12, Pp 40412-40424 (2024)
Staining of histological slides with Hematoxylin and Eosin is widely used in clinical and laboratory settings as these dyes reveal nuclear structures as well as cytoplasm and collagen. For cancer diagnosis, these slides are used to recognize tissues
Externí odkaz:
https://doaj.org/article/bc26758e0d1f4343bbdb9d93afedcbab