An Interactive Approach to Region of Interest Selection in Cytologic Analysis of Uveal Melanoma Based on Unsupervised Clustering

Haomin Chen, T. Y.Alvin Liu, Zelia Correa, Mathias Unberath

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Facilitating quantitative analysis of cytology images of fine needle aspirates of uveal melanoma is important to confirm diagnosis and inform management decisions. Extracting high-quality regions of interest (ROIs) from cytology whole slide images is a critical first step. To the best of our knowledge, we describe the first unsupervised clustering-based method for fine needle aspiration cytology (FNAC) that automatically suggests high-quality ROIs. Our method is integrated in a graphical user interface that allows for interactive refinement of ROI suggestions to tailor analysis to any specific specimen. We show that the proposed approach suggests ROIs that are in very good agreement with expert-extracted regions and demonstrate that interactive refinement results in the extraction of more high-quality regions compared to purely algorithmic extraction alone.

Original languageEnglish (US)
Title of host publicationOphthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsHuazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages114-124
Number of pages11
ISBN (Print)9783030634186
DOIs
StatePublished - 2020
Event6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: Oct 8 2020Oct 8 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12069 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period10/8/2010/8/20

Keywords

  • Coarse to fine
  • Human-computer interaction
  • Machine learning
  • Unsupervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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