loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Nils Brünggel ; Pascal Vallotton and Patrick Conway

Affiliation: Roche Diagnostics

Keyword(s): Machine Learning, Deep Learning, Semantic Segmentation, Hematology.

Abstract: In hematology the ability to count and analyze red blood cells (RBCs) is of major importance. Roche’s proprietary Bloodhound ® technology allows the automated printing and staining of slides to generate a monolayer of blood cells. While the RBCs are spread evenly, overlaps cannot be avoided completely. In the presence of such overlaps several tasks become problematic such as counting cells, quantifying the mean cellular volume or measuring cell shapes, critical for particular conditions such as anisocytosis (RBCs that are unequal in size) or rouleaux (clumps of RBCs that look like stacked coins). Modern deep learning models such as U-Net make it possible to accurately segment images given the appropriate training data (images and segmentation masks). The U-Net paper highlights the ability to train a model with only few images by applying data augmentation. We apply the learnings from their work and show that the mask creation can largely be automated: We collected images of free-stan ding RBCs, automatically segmented these using traditional image processing algorithms and combined these to generate artificial overlaps. We then used these images to train a model and show that it generalizes to real overlaps. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.191.108.168

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Brünggel, N.; Vallotton, P. and Conway, P. (2023). Segmenting Overlapping Red Blood Cells With Classical Image Processing and Deep Learning. In Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare - SDAIH; ISBN 978-989-758-629-3, SciTePress, pages 47-52. DOI: 10.5220/0011537000003523

@conference{sdaih23,
author={Nils Brünggel. and Pascal Vallotton. and Patrick Conway.},
title={Segmenting Overlapping Red Blood Cells With Classical Image Processing and Deep Learning},
booktitle={Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare - SDAIH},
year={2023},
pages={47-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011537000003523},
isbn={978-989-758-629-3},
}

TY - CONF

JO - Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare - SDAIH
TI - Segmenting Overlapping Red Blood Cells With Classical Image Processing and Deep Learning
SN - 978-989-758-629-3
AU - Brünggel, N.
AU - Vallotton, P.
AU - Conway, P.
PY - 2023
SP - 47
EP - 52
DO - 10.5220/0011537000003523
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic