SwePub
Sök i LIBRIS databas

  Extended search

onr:"swepub:oai:lup.lub.lu.se:e62a7f83-84d9-49f3-8e07-5260603905d6"
 

Search: onr:"swepub:oai:lup.lub.lu.se:e62a7f83-84d9-49f3-8e07-5260603905d6" > Integrating Color D...

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Integrating Color Deconvolution Thresholding and Weakly Supervised Learning for Automated Segmentation of Neurofibrillary Tangle and Neuropil Threads

Roh, Hyung S. (author)
University of Pennsylvania
Irwin, David J. (author)
University of Pennsylvania
López, Mónica Muñoz (author)
University of Castilla La Mancha
show more...
de Onzoño Martin, Maria Mercedes Iñiguez (author)
University of Castilla La Mancha
Ittyerah, Ranjit (author)
University of Pennsylvania
Lim, Sydney (author)
University of Pennsylvania
Bedard, Madigan L. (author)
University of Pennsylvania
Robinson, John L. (author)
University of Pennsylvania
Schuck, Theresa (author)
University of Pennsylvania
Artacho-Pérula, Emilio (author)
University of Castilla La Mancha
del Mar Arroyo Jiménez, María (author)
University of Castilla La Mancha
Rabal, María Pilar Marcos (author)
University of Castilla La Mancha
Romero, Francisco Javier Molina (author)
University of Castilla La Mancha
Sánchez, Sandra Cebada (author)
University of Castilla La Mancha
González, José Carlos Delgado (author)
University of Castilla La Mancha
de la Rosa-Prieto, Carlos (author)
University of Castilla La Mancha
Parada, Marta Córcoles (author)
University of Castilla La Mancha
Lee, Edward B. (author)
University of Pennsylvania
Ohm, Daniel T. (author)
University of Pennsylvania
Wisse, Laura E.M. (author)
Lund University,Lunds universitet,Diagnostisk radiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LU profilområde: Proaktivt åldrande,Lunds universitets profilområden,Diagnostic Radiology, (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,LU Profile Area: Proactive Ageing,Lund University Profile areas
Wolk, David A. (author)
University of Pennsylvania
Gee, James C. (author)
University of Pennsylvania
Insausti, Ricardo (author)
University of Castilla La Mancha
Yushkevich, Paul A. (author)
University of Pennsylvania
Chen, Min (author)
University of Pennsylvania
Tomaszewski, John E. (editor)
Ward, Aaron D. (editor)
show less...
 (creator_code:org_t)
2023
2023
English.
In: Medical Imaging 2023 : Digital and Computational Pathology - Digital and Computational Pathology. - 1605-7422. - 9781510660472 ; 12471
  • Conference paper (peer-reviewed)
Abstract Subject headings
Close  
  • Abnormally phosphorylated tau proteins are known to be a major indicator of Alzheimer's Disease (AD) with strong association with memory loss and cognitive decline. Automated generation of pixel-wise accurate neurofibrillary tangles (NFTs) and neuropil threads (NTs) segmentation is a challenging task, due to lack of ground truth segmentation data of these abnormal tau pathology. This problem is most prominent in the case of segmenting NTs, where the small threadlike morphology makes pixel-wise labeling a laborious task and unrealistic for large-scale studies. Lack of ground truth data poses a significant limitation for many learning-based methods to generate accurate segmentations of NFTs and NTs. This work presents an automated pipeline for pixel level segmentation of NFTs and NTs that does not rely on ground truth segmentation data. The pipeline is composed of four main steps: (1) color deconvolution is used to separate histopathology images into staining channels (DAB, Hematoxylin, and Eosin), (2) Otsu's thresholding is used on the DAB stain channel to generate pixel level segmentation of abnormal tau proteins staining, (3) a weakly-supervised learning paradigm (WildCat), using only global descriptors of images, is used to generate density maps of potential regions of NFTs and NTs, and (4) density maps and segmentations are then integrated using connected component analysis to localize NFTs and NTs in the detected tau segmentations. Our results show high global classification accuracy for NFTs (Acc:0.96) and NTs (Acc:0.91), and statistically significant distinctions when evaluating the percent area occupied of the detected NTs relative to expert ratings of NTs severity. Qualitative assessment of the NFTs and NTs results showed accurate pixel-level segmentations of the NFTs, while modest performance for NTs.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Image Processing (hsv//eng)

Keyword

Color Deconvolution
Histopathology Images
Neurofibrillary Tangles
Neuropil Threads
Otsu's Thresholding
Segmentation
Weakly Supervised Learning

Publication and Content Type

kon (subject category)
ref (subject category)

Find in a library

To the university's database

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view