Abstract
Background: The automatic segmentation of pseudohyphal cell-aggregates from brightfield microscopy images for counting forming cells is a challenging task due to the heterogeneous optical appearances of the cells as they may lie on different focal planes. The current cell counting method is based on a time-consuming manual counting of stained cells on a hemocytometer and in most cases, it represents estimates of low statistical significance due to the effort needed to prepare and analyze many samples. In this work, we evaluated the effectiveness of a marker-controlled watershed algorithm for automatic segmentation of pseudohyphae from brightfield microscopic images. The cell heterogeneity problem was addressed by processing intracellular contents of focused and defocused cells to extract initial foreground markers for the watershed method. By properly segmenting cells of different classes within a pseudohypha allows increasing the number of cells analyzed contributing thus to more reliable estimates. To facilitate the evaluation of the proposal by acquiring images containing a diversity of cells´ appearances, we utilized in situ microscopy, an imaging system used to capture images directly from suspensions.Results: The performance of the method was evaluated on 120 portraits of a yeast exhibiting a diversity of pseudohyphal morphologies. Automatic results were compared with manual references obtained by visual inspection of the images. Despite the simultaneous occurrence of a representative mixture of focused, over-, and under-focused cells, the method produced robust results with an average segmentation sensitivity, specificity, and accuracy of 76%, 89%, and 76%, respectively. On average, each microscopic image was processed within 3 s.Conclusions: Our approach was capable to segment pseudohyphae formed by cells exhibiting a large diversity of appearances. The application of a marker-controlled watershed algorithm as a simple, yet effective technique for segmenting pseudohyphae demonstrated satisfactory overall performance to support automated analysis of pseudohyphal cell-aggregates from brightfield images.
Deeply-supervised density regression for automatic cell counting in microscopy images
