Semantic versus instance segmentation in microscopic algae detection

Microscopic algae segmentation, specifically of diatoms, is an essential procedure for water quality assessment. The segmentation of these microalgae is still a challenge for computer vision. This paper addresses for the first time this problem using deep learning approaches to predict exactly those pixels that belong to each class, i.e., diatom and non diatom. A comparison between semantic segmentation and instance segmentation is carried out, and the performance of these methods is evaluated in the presence of different types of noise. The trained models are then evaluated with the same raw images used for manual diatom identification. A total of 126 images of the entire field of view at 60x magnification, with a size of 2592x1944 pixels, are analyzed. The images contain 10 different taxa plus debris and fragments. The best results were obtained with instance segmentation achieving an average precision of 85% with 86% sensitivity and 91% specificity (up to 92% precision with 98%, both sensitivity and specificity for some taxa). Semantic segmentation was able to improve the average sensitivity up to 95% but decreasing the specificity down to 60% and precision to 57%. Instance segmentation was also able to properly separate diatoms when overlap occurs, which helps estimate the number of diatoms, a key requirement for water quality grading.