Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images

Azam Hamidinekoo; Reyer Zwiggelaar

doi:10.1007/978-3-319-67558-9_25

Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images

Azam Hamidinekoo, Reyer Zwiggelaar

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The mitosis count, one of the main components considered for grading breast cancer on histology images, is used to assess tumour proliferation. On breast histology sections, different coloured stains are used to highlight existing cellular components. To keep the wealth of information in the stain colour representation and decrease the sensitivity of detection and classification models to stain variations, the RGB Histogram Specification method is used as a preprocessing step in the training of a modified deep convolutional neural network. Different models are trained on raw and stain normalised images using different databases. Evaluation results show more stable detection performance for various imaging conditions. Combining different data sources and employing stain colour normalisation and transfer learning, a network is trained that can be used for the general mitosis detection task and dealing with staining and scanner variations.

Iaith wreiddiol	Saesneg
Teitl	Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings
Is-deitl	Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings
Golygyddion	M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, João Manuel R.S. Tavares, Mehdi Moradi, Andrew Bradley, Hayit Greenspan, João Paulo Papa, Arnant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, Zhi Lu
Cyhoeddwr	Springer Nature
Tudalennau	213-221
Nifer y tudalennau	9
ISBN (Electronig)	978-3-319-67558-9
ISBN (Argraffiad)	978-3-319-67557-2
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/978-3-319-67558-9_25
Statws	Cyhoeddwyd - 09 Medi 2017
Digwyddiad	Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017 - Québec City, Canada Hyd: 14 Medi 2017 → …

Cyfres gyhoeddiadau

Enw	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Cyfrol	10553 LNCS
ISSN (Argraffiad)	0302-9743
ISSN (Electronig)	1611-3349

Cynhadledd

Cynhadledd	Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017
Gwlad/Tiriogaeth	Canada
Dinas	Québec City
Cyfnod	14 Medi 2017 → …

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10.1007/978-3-319-67558-9_25

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Hamidinekoo, A., & Zwiggelaar, R. (2017). Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images. Yn M. J. Cardoso, T. Arbel, G. Carneiro, J. M. R. S. Tavares, M. Moradi, A. Bradley, H. Greenspan, J. P. Papa, A. Madabhushi, J. C. Nascimento, J. S. Cardoso, V. Belagiannis, & Z. Lu (Gol.), Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings (tt. 213-221). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Cyfrol 10553 LNCS). Springer Nature. https://doi.org/10.1007/978-3-319-67558-9_25

Hamidinekoo, Azam ; Zwiggelaar, Reyer. / Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings. Gol. / M. Jorge Cardoso ; Tal Arbel ; Gustavo Carneiro ; João Manuel R.S. Tavares ; Mehdi Moradi ; Andrew Bradley ; Hayit Greenspan ; João Paulo Papa ; Arnant Madabhushi ; Jacinto C. Nascimento ; Jaime S. Cardoso ; Vasileios Belagiannis ; Zhi Lu. Springer Nature, 2017. tt. 213-221 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0aaa0eaf502a48d3ad907b300e0ef3e2,

title = "Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images",

abstract = "The mitosis count, one of the main components considered for grading breast cancer on histology images, is used to assess tumour proliferation. On breast histology sections, different coloured stains are used to highlight existing cellular components. To keep the wealth of information in the stain colour representation and decrease the sensitivity of detection and classification models to stain variations, the RGB Histogram Specification method is used as a preprocessing step in the training of a modified deep convolutional neural network. Different models are trained on raw and stain normalised images using different databases. Evaluation results show more stable detection performance for various imaging conditions. Combining different data sources and employing stain colour normalisation and transfer learning, a network is trained that can be used for the general mitosis detection task and dealing with staining and scanner variations.",

author = "Azam Hamidinekoo and Reyer Zwiggelaar",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017 ; Conference date: 14-09-2017",

year = "2017",

month = sep,

day = "9",

doi = "10.1007/978-3-319-67558-9_25",

language = "English",

isbn = "978-3-319-67557-2",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Nature",

pages = "213--221",

editor = "Cardoso, {M. Jorge} and Tal Arbel and Gustavo Carneiro and Tavares, {Jo{\~a}o Manuel R.S.} and Mehdi Moradi and Andrew Bradley and Hayit Greenspan and Papa, {Jo{\~a}o Paulo} and Arnant Madabhushi and Nascimento, {Jacinto C.} and Cardoso, {Jaime S.} and Vasileios Belagiannis and Zhi Lu",

booktitle = "Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings",

address = "Switzerland",

}

Hamidinekoo, A & Zwiggelaar, R 2017, Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images. yn MJ Cardoso, T Arbel, G Carneiro, JMRS Tavares, M Moradi, A Bradley, H Greenspan, JP Papa, A Madabhushi, JC Nascimento, JS Cardoso, V Belagiannis & Z Lu (gol.), Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), cyfrol. 10553 LNCS, Springer Nature, tt. 213-221, Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, Quebec, Canada, 14 Medi 2017. https://doi.org/10.1007/978-3-319-67558-9_25

Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images. / Hamidinekoo, Azam; Zwiggelaar, Reyer.
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings. gol. / M. Jorge Cardoso; Tal Arbel; Gustavo Carneiro; João Manuel R.S. Tavares; Mehdi Moradi; Andrew Bradley; Hayit Greenspan; João Paulo Papa; Arnant Madabhushi; Jacinto C. Nascimento; Jaime S. Cardoso; Vasileios Belagiannis; Zhi Lu. Springer Nature, 2017. t. 213-221 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Cyfrol 10553 LNCS).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

TY - GEN

T1 - Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images

AU - Hamidinekoo, Azam

AU - Zwiggelaar, Reyer

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Y1 - 2017/9/9

N2 - The mitosis count, one of the main components considered for grading breast cancer on histology images, is used to assess tumour proliferation. On breast histology sections, different coloured stains are used to highlight existing cellular components. To keep the wealth of information in the stain colour representation and decrease the sensitivity of detection and classification models to stain variations, the RGB Histogram Specification method is used as a preprocessing step in the training of a modified deep convolutional neural network. Different models are trained on raw and stain normalised images using different databases. Evaluation results show more stable detection performance for various imaging conditions. Combining different data sources and employing stain colour normalisation and transfer learning, a network is trained that can be used for the general mitosis detection task and dealing with staining and scanner variations.

AB - The mitosis count, one of the main components considered for grading breast cancer on histology images, is used to assess tumour proliferation. On breast histology sections, different coloured stains are used to highlight existing cellular components. To keep the wealth of information in the stain colour representation and decrease the sensitivity of detection and classification models to stain variations, the RGB Histogram Specification method is used as a preprocessing step in the training of a modified deep convolutional neural network. Different models are trained on raw and stain normalised images using different databases. Evaluation results show more stable detection performance for various imaging conditions. Combining different data sources and employing stain colour normalisation and transfer learning, a network is trained that can be used for the general mitosis detection task and dealing with staining and scanner variations.

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Hamidinekoo A, Zwiggelaar R. Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images. Yn Cardoso MJ, Arbel T, Carneiro G, Tavares JMRS, Moradi M, Bradley A, Greenspan H, Papa JP, Madabhushi A, Nascimento JC, Cardoso JS, Belagiannis V, Lu Z, golygyddion, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings. Springer Nature. 2017. t. 213-221. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2017 Medi 7. doi: 10.1007/978-3-319-67558-9_25

Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images

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