Learning Melanocytic Cell Masks from Adjacent Stained Tissue

Document detail

ID

oai:arXiv.org:2211.00646

Topic

Quantitative Biology - Quantitativ... Computer Science - Artificial Inte... Computer Science - Computer Vision... Computer Science - Machine Learnin... Electrical Engineering and Systems...

Author

Tada, Mikio Lang, Ursula E. Yeh, Iwei Keiser, Elizabeth S. Wei, Maria L. Keiser, Michael J.

Year

2022

listing date

3/20/2024

Keywords

melanoma image cancer learning melanocytic cell computer science

Metrics

Abstract

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.

;Comment: Accepted at Medical Image Learning with Limited & Noisy Data Workshop, Medical Image Computing and Computer Assisted Interventions (MICCAI) 2022

Tada, Mikio,Lang, Ursula E.,Yeh, Iwei,Keiser, Elizabeth S.,Wei, Maria L.,Keiser, Michael J., 2022, Learning Melanocytic Cell Masks from Adjacent Stained Tissue