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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2211.00646 |
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| _version_ | 1866911796561969152 |
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| author | Tada, Mikio Lang, Ursula E. Yeh, Iwei Keiser, Elizabeth S. Wei, Maria L. Keiser, Michael J. |
| author_facet | Tada, Mikio Lang, Ursula E. Yeh, Iwei Keiser, Elizabeth S. Wei, Maria L. Keiser, Michael J. |
| contents | Melanoma is one of the most aggressive forms of skin cancer, causing a large proportion of skin cancer deaths. However, melanoma diagnoses by pathologists shows low interrater reliability. As melanoma is a cancer of the melanocyte, there is a clear need to develop a melanocytic cell segmentation tool that is agnostic to pathologist variability and automates pixel-level annotation. Gigapixel-level pathologist labeling, however, is impractical. Herein, we propose a means to train deep neural networks for melanocytic cell segmentation from hematoxylin and eosin (H&E) stained sections and paired immunohistochemistry (IHC) of adjacent tissue sections, achieving a mean IOU of 0.64 despite imperfect ground-truth labels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_00646 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Learning Melanocytic Cell Masks from Adjacent Stained Tissue Tada, Mikio Lang, Ursula E. Yeh, Iwei Keiser, Elizabeth S. Wei, Maria L. Keiser, Michael J. Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Melanoma is one of the most aggressive forms of skin cancer, causing a large proportion of skin cancer deaths. However, melanoma diagnoses by pathologists shows low interrater reliability. As melanoma is a cancer of the melanocyte, there is a clear need to develop a melanocytic cell segmentation tool that is agnostic to pathologist variability and automates pixel-level annotation. Gigapixel-level pathologist labeling, however, is impractical. Herein, we propose a means to train deep neural networks for melanocytic cell segmentation from hematoxylin and eosin (H&E) stained sections and paired immunohistochemistry (IHC) of adjacent tissue sections, achieving a mean IOU of 0.64 despite imperfect ground-truth labels. |
| title | Learning Melanocytic Cell Masks from Adjacent Stained Tissue |
| topic | Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2211.00646 |