A small note on variation in segmentation annotations

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Standard

A small note on variation in segmentation annotations. / Ørting, Silas Nyboe.

I: arXiv, 03.12.2020.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning

Harvard

Ørting, SN 2020, 'A small note on variation in segmentation annotations', arXiv. <http://arxiv.org/pdf/2012.01975v1>

APA

Ørting, S. N. (2020). A small note on variation in segmentation annotations. arXiv. http://arxiv.org/pdf/2012.01975v1

Vancouver

Ørting SN. A small note on variation in segmentation annotations. arXiv. 2020 dec. 3.

Author

Ørting, Silas Nyboe. / A small note on variation in segmentation annotations. I: arXiv. 2020.

Bibtex

@article{cc482e3576684cf8a56c4097dcba5c91,
title = "A small note on variation in segmentation annotations",
abstract = " We report on the results of a small crowdsourcing experiment conducted at a workshop on machine learning for segmentation held at the Danish Bio Imaging network meeting 2020. During the workshop we asked participants to manually segment mitochondria in three 2D patches. The aim of the experiment was to illustrate that manual annotations should not be seen as the ground truth, but as a reference standard that is subject to substantial variation. In this note we show how the large variation we observed in the segmentations can be reduced by removing the annotators with worst pair-wise agreement. Having removed the annotators with worst performance, we illustrate that the remaining variance is semantically meaningful and can be exploited to obtain segmentations of cell boundary and cell interior. ",
keywords = "cs.CV, cs.HC",
author = "{\O}rting, {Silas Nyboe}",
year = "2020",
month = dec,
day = "3",
language = "English",
journal = "arXiv",

}

RIS

TY - JOUR

T1 - A small note on variation in segmentation annotations

AU - Ørting, Silas Nyboe

PY - 2020/12/3

Y1 - 2020/12/3

N2 - We report on the results of a small crowdsourcing experiment conducted at a workshop on machine learning for segmentation held at the Danish Bio Imaging network meeting 2020. During the workshop we asked participants to manually segment mitochondria in three 2D patches. The aim of the experiment was to illustrate that manual annotations should not be seen as the ground truth, but as a reference standard that is subject to substantial variation. In this note we show how the large variation we observed in the segmentations can be reduced by removing the annotators with worst pair-wise agreement. Having removed the annotators with worst performance, we illustrate that the remaining variance is semantically meaningful and can be exploited to obtain segmentations of cell boundary and cell interior.

AB - We report on the results of a small crowdsourcing experiment conducted at a workshop on machine learning for segmentation held at the Danish Bio Imaging network meeting 2020. During the workshop we asked participants to manually segment mitochondria in three 2D patches. The aim of the experiment was to illustrate that manual annotations should not be seen as the ground truth, but as a reference standard that is subject to substantial variation. In this note we show how the large variation we observed in the segmentations can be reduced by removing the annotators with worst pair-wise agreement. Having removed the annotators with worst performance, we illustrate that the remaining variance is semantically meaningful and can be exploited to obtain segmentations of cell boundary and cell interior.

KW - cs.CV

KW - cs.HC

M3 - Journal article

JO - arXiv

JF - arXiv

ER -

ID: 253071786