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Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation Publisher



Nofallah S1 ; Li B2 ; Mokhtari M3 ; Wu W4 ; Knezevich S5 ; May CJ6 ; Chang OH7 ; Elmore JG8 ; Shapiro LG1, 2, 4
Authors
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Authors Affiliations
  1. 1. Department of Electrical and Computer Engineering, University of Washington, Seattle, 98195, WA, United States
  2. 2. Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, 98195, WA, United States
  3. 3. Pathology Department, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran
  4. 4. Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, 98195, WA, United States
  5. 5. Pathology Associates, Clovis, 983611, CA, United States
  6. 6. Dermatopathology Northwest, Bellevue, 98005, WA, United States
  7. 7. Department of Pathology, University of Washington, Seattle, 98195, WA, United States
  8. 8. David Geffen School of Medicine, UCLA, Los Angeles, 90024, CA, United States

Source: Diagnostics Published:2022


Abstract

Invasive melanoma, a common type of skin cancer, is considered one of the deadliest. Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if the lesion represents an invasive melanoma, its stage. However, due to the complicated nature of these assessments, inter- and intra-observer variability among pathologists in their interpretation are very common. Machine-learning techniques have shown impressive and robust performance on various tasks including healthcare. In this work, we study the potential of including semantic segmentation of clinically important tissue structure in improving the diagnosis of skin biopsy images. Our experimental results show a 6% improvement in F-score when using whole slide images along with epidermal nests and cancerous dermal nest segmentation masks compared to using whole-slide images alone in training and testing the diagnosis pipeline. © 2022 by the authors.
Related Docs
1. Segmenting Skin Biopsy Images With Coarse and Sparse Annotations Using U-Net, Journal of Digital Imaging (2022)
2. Learning Melanocytic Proliferation Segmentation in Histopathology Images From Imperfect Annotations, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2021)
3. Computer Aided Measurement of Melanoma Depth of Invasion in Microscopic Images, Micron (2014)
4. Skin Melanoma Detection in Microscopic Images Using Hmm-Based Asymmetric Analysis and Expectation Maximization, IEEE Journal of Biomedical and Health Informatics (2021)