Data

Histopathology Non-Melanoma Skin Cancer Segmentation Dataset

The University of Queensland
Dr Nicholas Hamilton (Author) Dr Simon Thomas (Author) Mr Simon Thomas (Author)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.14264/8be4bd0&rft.title=Histopathology Non-Melanoma Skin Cancer Segmentation Dataset&rft.identifier=10.14264/8be4bd0&rft.publisher=The University of Queensland&rft.description=A collection of 290 images of non-melanoma skin cancer H&E tissue sections and hand-annotated segmentation masks. Access to a pre-existing collection of skin cancer slides was provided by MyLab Pathology (Salisbury, Australia). A pathologist selected 290 slides and specific tissue sections which were representative of typical cases of non-melanoma skin cancer. The cancer classes are Basal Cell Carcinoma (BCC - 140), Squamous Cell Carcinoma (SCC - 60) and Intra-Epidermal Carcinoma (IEC - 90). The set includes shave biopsies (100), punch biopsies (58) and excisional biopsies (132). The slides were produced using xylene processing and paraffin wax, and imaged over four months in late 2017 and early 2018. The slides were sourced from patients between the ages of 34 and 96, with a median age of 70 years. Female and male proportions were 2/3 and 1/3, respectively, closely reflecting the prevalence of non-melanoma skin cancer in the Australian population ( Staples et al., 2006 ). The slides were imaged using a DP27 Olympus microscope camera using the 10x magnification lens with the light condenser attached. Individual images were stitched together to build a high-resolution mosaic using software available at https://github.com/smthomas-sci/HistoImageStitcher . The resulting images had a resolution where 1 pixel corresponds to 0.67μm in length. The images are stored in TIF format. The segmentation masks were created in ImageJ, using colors to classify pixels into 12 tissue classes: Glands (GLD), Inflammation (INF), Hair Follicles (FOL), Hypodermis (HYP), Reticular Dermis (RET), Papillary Dermis (PAP), Epidermis (EPI), Keratin (KER), Background (BKG), BCC, SCC, and IEC. The color legend is available in the repository and the masks are stored in PNG format. The data are provided at smaller resolutions (2x, 5x and 10x downsample factors) as well as the original (1x). Cancer margin data is also available, which consist of (x,y) coordinates for the cancer margins for each image in CSV format. The training, validation and testing sets are provided to support benchmarking, and are the same used by Thomas et al. (2021). References: Staples, M.P. , Elwood, M. , Burton, R.C. , Williams, J.L. , Marks, R. , Giles, G.G. , 2006. Non-melanoma skin cancer in Australia: the 2002 national survey and trends since 1985. Med. J. Aust. 184, 6-10 . Thomas, S. M., Lefevre, J. G., Baxter, G., & Hamilton, N. A. , 2021. Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer. Medical Image Analysis, 68, 101915.&rft.creator=Dr Nicholas Hamilton&rft.creator=Dr Simon Thomas&rft.creator=Mr Simon Thomas&rft.date=2021&rft_subject=non-Melanoma skin cancer segmentation&rft_subject=skin cancer segmentation imaging&rft.type=dataset&rft.language=English Access the data

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s4200058@student.uq.edu.au

Full description

A collection of 290 images of non-melanoma skin cancer H&E tissue sections and hand-annotated segmentation masks. Access to a pre-existing collection of skin cancer slides was provided by MyLab Pathology (Salisbury, Australia). A pathologist selected 290 slides and specific tissue sections which were representative of typical cases of non-melanoma skin cancer. The cancer classes are Basal Cell Carcinoma (BCC - 140), Squamous Cell Carcinoma (SCC - 60) and Intra-Epidermal Carcinoma (IEC - 90). The set includes shave biopsies (100), punch biopsies (58) and excisional biopsies (132). The slides were produced using xylene processing and paraffin wax, and imaged over four months in late 2017 and early 2018. The slides were sourced from patients between the ages of 34 and 96, with a median age of 70 years. Female and male proportions were 2/3 and 1/3, respectively, closely reflecting the prevalence of non-melanoma skin cancer in the Australian population ( Staples et al., 2006 ). The slides were imaged using a DP27 Olympus microscope camera using the 10x magnification lens with the light condenser attached. Individual images were stitched together to build a high-resolution mosaic using software available at https://github.com/smthomas-sci/HistoImageStitcher . The resulting images had a resolution where 1 pixel corresponds to 0.67μm in length. The images are stored in TIF format. The segmentation masks were created in ImageJ, using colors to classify pixels into 12 tissue classes: Glands (GLD), Inflammation (INF), Hair Follicles (FOL), Hypodermis (HYP), Reticular Dermis (RET), Papillary Dermis (PAP), Epidermis (EPI), Keratin (KER), Background (BKG), BCC, SCC, and IEC. The color legend is available in the repository and the masks are stored in PNG format. The data are provided at smaller resolutions (2x, 5x and 10x downsample factors) as well as the original (1x). Cancer margin data is also available, which consist of (x,y) coordinates for the cancer margins for each image in CSV format. The training, validation and testing sets are provided to support benchmarking, and are the same used by Thomas et al. (2021). References: Staples, M.P. , Elwood, M. , Burton, R.C. , Williams, J.L. , Marks, R. , Giles, G.G. , 2006. Non-melanoma skin cancer in Australia: the 2002 national survey and trends since 1985. Med. J. Aust. 184, 6-10 . Thomas, S. M., Lefevre, J. G., Baxter, G., & Hamilton, N. A. , 2021. Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer. Medical Image Analysis, 68, 101915.

Issued: 2021

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