Sashimi: A toolkit for facilitating high-throughput organismal image segmentation using deep learning

1. Digitized specimens are an indispensable resource for rapidly acquiring
big datasets and typically must be preprocessed prior to conducting
analyses. One crucial image preprocessing step in any image analysis
workflow is image segmentation, or the ability to clearly contrast the
foreground target from the background noise in an image. This procedure is
typically done manually, creating a potential bottleneck for efforts to
quantify biodiversity from image databases. Image segmentation
meta-algorithms using deep learning provide an opportunity to relax this
bottleneck. However, the most accessible pre-trained convolutional neural
networks (CNNs) have been trained on a small fraction of biodiversity,
thus limiting their utility. 2. We trained a deep learning model to
automatically segment target fish from images with both standardized and
complex, noisy backgrounds. We then assessed the performance of our deep
learning model using qualitative visual inspection and quantitative image
segmentation metrics of pixel overlap between reference segmentation masks
generated manually by experts and those automatically predicted by our
model. 3. Visual inspection revealed that our model segmented fishes with
high precision and relatively few artifacts. These results suggest that
the meta-algorithm (Mask R-CNN), in which our current fish segmentation
model relies on, is well-suited for generating high-fidelity segmented
specimen images across a variety of background contexts at rapid pace. 4.
We present Sashimi, a user-friendly command line toolkit to facilitate
rapid, automated high-throughput image segmentation of digitized
organisms. Sashimi is accessible to non-programmers and does not require
experience with deep learning to use. The flexibility of Mask R-CNN allows
users to generate a segmentation model for use on diverse animal and plant
images using transfer learning with training datasets as small as a few
hundred images. To help grow the taxonomic scope of images that can be
recognized, Sashimi also includes a central database for sharing and
distributing custom-trained segmentation models of other unrepresented
organisms. Lastly, Sashimi includes both auxiliary image preprocessing
functions useful for some popular downstream color pattern analysis
workflows, as well as a simple script to aid users in qualitatively and
quantitatively assessing segmentation model performance for complementary
sets of automatically and manually segmented images.