Scoring of breast tissue microarrays using ordinal regression: local patches vs. nuclei segmentation

Telmo Amaral; Michele Sciarabba; Katherine Robertson; Alastair Thompson; Stephen McKenna

Scoring of breast tissue microarrays using ordinal regression: local patches vs. nuclei segmentation

Telmo Amaral, Michele Sciarabba, Katherine Robertson, Alastair Thompson, Stephen McKenna

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Breast tissue microarrays (TMAs) facilitate the study of very large numbers of breast tumours per histological section, but their scoring by pathologists is time consuming, prone to observer variability, and not without error. This paper reports the use of ordinal regression to predict the scores of TMA spots subjected to progesterone receptor immunohistochemistry. We compare the use of global features obtained via two different methods, one involving and the other dispensing with accurate segmentation of epithelial cell nuclei. In addition, we investigate the effect of analysing only regions of interest (ROIs) within each spot, as opposed to analysing the whole spots. The use of the entropy of the posterior probability distribution over category labels for avoiding uncertain decisions is demonstrated.

Original language	English
Title of host publication	Medical Image Understanding and Analysis
Publisher	British Machine Vision Association and Society for Pattern Recognition
Number of pages	6
Publication status	Published - 2009

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@inproceedings{9b19080750e5401e8ee64332c1203f3c,

title = "Scoring of breast tissue microarrays using ordinal regression: local patches vs. nuclei segmentation",

abstract = "Breast tissue microarrays (TMAs) facilitate the study of very large numbers of breast tumours per histological section, but their scoring by pathologists is time consuming, prone to observer variability, and not without error. This paper reports the use of ordinal regression to predict the scores of TMA spots subjected to progesterone receptor immunohistochemistry. We compare the use of global features obtained via two different methods, one involving and the other dispensing with accurate segmentation of epithelial cell nuclei. In addition, we investigate the effect of analysing only regions of interest (ROIs) within each spot, as opposed to analysing the whole spots. The use of the entropy of the posterior probability distribution over category labels for avoiding uncertain decisions is demonstrated.",

author = "Telmo Amaral and Michele Sciarabba and Katherine Robertson and Alastair Thompson and Stephen McKenna",

year = "2009",

language = "English",

booktitle = "Medical Image Understanding and Analysis",

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Scoring of breast tissue microarrays using ordinal regression: local patches vs. nuclei segmentation. / Amaral, Telmo; Sciarabba, Michele; Robertson, Katherine et al.
Medical Image Understanding and Analysis. British Machine Vision Association and Society for Pattern Recognition, 2009.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Scoring of breast tissue microarrays using ordinal regression

T2 - local patches vs. nuclei segmentation

AU - Amaral, Telmo

AU - Sciarabba, Michele

AU - Robertson, Katherine

AU - Thompson, Alastair

AU - McKenna, Stephen

PY - 2009

Y1 - 2009

N2 - Breast tissue microarrays (TMAs) facilitate the study of very large numbers of breast tumours per histological section, but their scoring by pathologists is time consuming, prone to observer variability, and not without error. This paper reports the use of ordinal regression to predict the scores of TMA spots subjected to progesterone receptor immunohistochemistry. We compare the use of global features obtained via two different methods, one involving and the other dispensing with accurate segmentation of epithelial cell nuclei. In addition, we investigate the effect of analysing only regions of interest (ROIs) within each spot, as opposed to analysing the whole spots. The use of the entropy of the posterior probability distribution over category labels for avoiding uncertain decisions is demonstrated.

AB - Breast tissue microarrays (TMAs) facilitate the study of very large numbers of breast tumours per histological section, but their scoring by pathologists is time consuming, prone to observer variability, and not without error. This paper reports the use of ordinal regression to predict the scores of TMA spots subjected to progesterone receptor immunohistochemistry. We compare the use of global features obtained via two different methods, one involving and the other dispensing with accurate segmentation of epithelial cell nuclei. In addition, we investigate the effect of analysing only regions of interest (ROIs) within each spot, as opposed to analysing the whole spots. The use of the entropy of the posterior probability distribution over category labels for avoiding uncertain decisions is demonstrated.

M3 - Conference contribution

BT - Medical Image Understanding and Analysis

PB - British Machine Vision Association and Society for Pattern Recognition

ER -

Scoring of breast tissue microarrays using ordinal regression: local patches vs. nuclei segmentation

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