Non-melanoma skin cancer segmentation for histopathology dataset

Densely labelled segmentation data for digital pathology images is costly to produce but is invaluable to training effective machine learning models. We make available 290 hand-annotated histopathology tissue sections of the 3 most common skin cancers; basal cell carcinoma (BCC), squamous cell carci...

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Autores principales:	Simon M. Thomas, James G. Lefevre, Glenn Baxter, Nicholas A. Hamilton
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Digital pathology Machine learning Medical imaging Image analysis Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/f31868d8272840e993f554c05907ffb6
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spelling	oai:doaj.org-article:f31868d8272840e993f554c05907ffb62021-11-24T04:31:28ZNon-melanoma skin cancer segmentation for histopathology dataset2352-340910.1016/j.dib.2021.107587https://doaj.org/article/f31868d8272840e993f554c05907ffb62021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352340921008623https://doaj.org/toc/2352-3409Densely labelled segmentation data for digital pathology images is costly to produce but is invaluable to training effective machine learning models. We make available 290 hand-annotated histopathology tissue sections of the 3 most common skin cancers; basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and intraepidermal carcinoma (IEC). These non-melanoma skin cancers constitute over 90% of all skin cancer diagnoses and hence this dataset gives an opportunity to the scientific community to benchmark analytic methodologies on a significant portion of the dermatopathology workflow. The data represents typical cases of the three cancer types (not requiring a differential diagnosis) across shave, punch and excision biopsy contexts. Each image is accompanied with a segmentation mask which characterizes the section into 12 tissue types, specifically: keratin, epidermis, papillary dermis, reticular dermis, hypodermis, inflammation, glands, hair follicles and background, as well as BCC, SCC and IEC. Included also are cancer margin measurements to work towards automated assessment of surgical margin clearance and tumour invasion. This leaves open many opportunities for researchers to utilize or extend the dataset, building upon recent work on image analysis problems in skin cancer (Thomas et al., 2021).Simon M. ThomasJames G. LefevreGlenn BaxterNicholas A. HamiltonElsevierarticleDigital pathologyMachine learningMedical imagingImage analysisComputer applications to medicine. Medical informaticsR858-859.7Science (General)Q1-390ENData in Brief, Vol 39, Iss , Pp 107587- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Digital pathology Machine learning Medical imaging Image analysis Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
spellingShingle	Digital pathology Machine learning Medical imaging Image analysis Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 Simon M. Thomas James G. Lefevre Glenn Baxter Nicholas A. Hamilton Non-melanoma skin cancer segmentation for histopathology dataset
description	Densely labelled segmentation data for digital pathology images is costly to produce but is invaluable to training effective machine learning models. We make available 290 hand-annotated histopathology tissue sections of the 3 most common skin cancers; basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and intraepidermal carcinoma (IEC). These non-melanoma skin cancers constitute over 90% of all skin cancer diagnoses and hence this dataset gives an opportunity to the scientific community to benchmark analytic methodologies on a significant portion of the dermatopathology workflow. The data represents typical cases of the three cancer types (not requiring a differential diagnosis) across shave, punch and excision biopsy contexts. Each image is accompanied with a segmentation mask which characterizes the section into 12 tissue types, specifically: keratin, epidermis, papillary dermis, reticular dermis, hypodermis, inflammation, glands, hair follicles and background, as well as BCC, SCC and IEC. Included also are cancer margin measurements to work towards automated assessment of surgical margin clearance and tumour invasion. This leaves open many opportunities for researchers to utilize or extend the dataset, building upon recent work on image analysis problems in skin cancer (Thomas et al., 2021).
format	article
author	Simon M. Thomas James G. Lefevre Glenn Baxter Nicholas A. Hamilton
author_facet	Simon M. Thomas James G. Lefevre Glenn Baxter Nicholas A. Hamilton
author_sort	Simon M. Thomas
title	Non-melanoma skin cancer segmentation for histopathology dataset
title_short	Non-melanoma skin cancer segmentation for histopathology dataset
title_full	Non-melanoma skin cancer segmentation for histopathology dataset
title_fullStr	Non-melanoma skin cancer segmentation for histopathology dataset
title_full_unstemmed	Non-melanoma skin cancer segmentation for histopathology dataset
title_sort	non-melanoma skin cancer segmentation for histopathology dataset
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/f31868d8272840e993f554c05907ffb6
work_keys_str_mv	AT simonmthomas nonmelanomaskincancersegmentationforhistopathologydataset AT jamesglefevre nonmelanomaskincancersegmentationforhistopathologydataset AT glennbaxter nonmelanomaskincancersegmentationforhistopathologydataset AT nicholasahamilton nonmelanomaskincancersegmentationforhistopathologydataset
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Non-melanoma skin cancer segmentation for histopathology dataset

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