A Thrifty Annotation Generation Approach for Semantic Segmentation of Biofilms

Adithi D. Chakravarthy, Parvathi Chundi, Mahadevan Subramaniam, Shankarachary Ragi, Venkata R. Gadhamshetty

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

Abstract

Recent advances in semantic segmentation using deep learning methods have achieved promising results on several benchmark datasets. However, the primary challenge involved in such segmentation approaches is the availability of applicable training data. Since only experts are equipped to effectively annotate (or label) any available data for training semantic segmentation networks, the effort and cost involved can be considerable, especially for larger datasets. In this paper, we aim to address this problem by proposing a Thrifty Annotation Generation approach that records high performance on segmentation networks with minimal expert effort and cost (intervention). We present a deep active learning framework that combines the use of marker-controlled watershed (MC-WS) algorithm to generate pseudo labels for segmentation networks (U-Net) and active learning to significantly minimize effort and cost by selecting only the most impactful training data for labeling. We built the initial U-Net model by generating pseudo labels for the training data using MC-WS. We then make use of the uncertainty information (entropy) of each image provided by the U-Net to determine the most uncertain or effective images for expert labeling. We evaluated the TAG approach using the 2012 ISBI Challenge dataset for 2D segmentation and a novel Biofilm dataset. Our approach achieved promising segmentation accuracy (IoU) and classification accuracy with minimal expert intervention. The results of our experiments also indicate that the TAG approach can be generalized to achieve high-performance segmentation results on any dataset using minimal expert effort and cost.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages602-607
Number of pages6
ISBN (Electronic)9781728195742
DOIs
StatePublished - Oct 2020
Event20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 - Virtual, Cincinnati, United States
Duration: Oct 26 2020Oct 28 2020

Publication series

NameProceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020

Conference

Conference20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020
Country/TerritoryUnited States
CityVirtual, Cincinnati
Period10/26/2010/28/20

Keywords

  • Biofilms
  • Pseudo labels
  • Semantic segmentation
  • Watershed algorithm
  • and Active learning

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Molecular Biology
  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Modeling and Simulation
  • Health Informatics

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