Coarse-to-fine foraminifera image segmentation through 3D and deep features

Foraminifera are single-celled marine organisms, which are usually less than 1 mm in diameter. One of the most common tasks associated with foraminifera is the species identification of thousands of foraminifera contained in rock or ocean sediment samples, which can be a tedious manual procedure. Thus an automatic visual identification system is desirable. Some of the primary criteria for foraminifera species identification come from the characteristics of the shell itself. As such, segmentation of chambers and apertures in foraminifera images would provide powerful features for species identification. Nevertheless, none of the existing image-based, automatic classification approaches make use of segmentation, partly due to the lack of accurate segmentation methods for foraminifera images. In this paper, we propose a learning-based edge detection pipeline, using a coarse-to-fine strategy, to extract the vague edges from foraminifera images for segmentation using a relatively small training set. The experiments demonstrate our approach is able to segment chambers and apertures of foraminifera correctly and has the potential to provide useful features for species identification and other applications such as morphological study of foraminifera shells and foraminifera dataset labeling.