Segmenting Skin Biopsy Images With Coarse and Sparse Annotations Using U-Net Publisher Pubmed



Nofallah S¹ ; Mokhtari M² ; Wu W¹ ; Mehta S¹ ; Knezevich S³ ; May CJ⁴ ; Chang OH¹ ; Lee AC⁵ ; Elmore JG⁵ ; Shapiro LG¹ Show Affiliations
Source: Journal of Digital Imaging Published:2022

Abstract

The number of melanoma diagnoses has increased dramatically over the past three decades, outpacing almost all other cancers. Nearly 1 in 4 skin biopsies is of melanocytic lesions, highlighting the clinical and public health importance of correct diagnosis. Deep learning image analysis methods may improve and complement current diagnostic and prognostic capabilities. The histologic evaluation of melanocytic lesions, including melanoma and its precursors, involves determining whether the melanocytic population involves the epidermis, dermis, or both. Semantic segmentation of clinically important structures in skin biopsies is a crucial step towards an accurate diagnosis. While training a segmentation model requires ground-truth labels, annotation of large images is a labor-intensive task. This issue becomes especially pronounced in a medical image dataset in which expert annotation is the gold standard. In this paper, we propose a two-stage segmentation pipeline using coarse and sparse annotations on a small region of the whole slide image as the training set. Segmentation results on whole slide images show promising performance for the proposed pipeline. © 2022, The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.