Computational Metadata Generation Methods for Biological Specimen Image Collections

Abstract

Metadata is a key data source for researchers seeking to apply machine learning (ML) to the vast collections of digitized biological specimens that can be found online. Unfortunately, the available metadata is often sparse and, at times, erroneous. This paper extends previous research with the Illinois Natural History Survey (INHS) collection (7,244 specimen images) using computational approaches to analyze image quality, and then automatically generate 22 metadata properties representing the image quality and morphological features of the specimens. In the research reported here, we demonstrate the extension of our initial work to University of Wisconsin Zoological Museum (UWZM) collection (4,155 specimen images). Further, we enhance our computational methods in four ways: 1) augmenting the training set, 2) applying contrast enhancement, 3) upscaling small objects, and 4) refining of our processing logic. Together these new methods improved our overall error rates from 4.6% to 1.1%. These enhancements also allowed us to compute an additional set of 17 image-based metadata properties. The new metadata properties provide supplemental features and information that may also be used to analyze and classify the fish specimens. Examples of these new features include convex area, eccentricity, perimeter, skew, etc. The newly refined process further outperforms humans in terms of time and labor cost, as well as accuracy, providing a novel solution for leveraging digitized specimens with ML. This research demonstrates the ability of computational methods to enhance the digital library services associated with the tens of thousands of digitized specimens stored in open-access repositories world-wide by generating accurate and valuable metadata for those repositories.

A FAIR and modular image‐based workflow for knowledge discovery in the emerging field of imageomics

“…The first ML component (Figures 1 and 2, step 4a) performs object detection and metadata generation as defined by the rule generate_metadata from the BGNN_Core_Workflow (Tabarin, Bradley, Balk, & Lapp, 2023a; Table 1). This rule calls the container drexel_metadata from the drexel_metadata repository (Karnani et al., 2023; Table 1), which generates a metadata file for each image datum (named ARKID.json). The codebase has two outputs and is described in Leipzig et al.…”