  Despite recent advances in data-independent and deep-learning algorithms,
unstained live adherent cell instance segmentation remains a long-standing
challenge in cell image processing. Adherent cells' inherent visual
characteristics, such as low contrast structures, fading edges, and irregular
morphology, have made it difficult to distinguish from one another, even by
human experts, let alone computational methods. In this study, we developed a
novel deep-learning algorithm called dual-view selective instance segmentation
network (DVSISN) for segmenting unstained adherent cells in differential
interference contrast (DIC) images. First, we used a dual-view segmentation
(DVS) method with pairs of original and rotated images to predict the bounding
box and its corresponding mask for each cell instance. Second, we used a mask
selection (MS) method to filter the cell instances predicted by the DVS to keep
masks closest to the ground truth only. The developed algorithm was trained and
validated on our dataset containing 520 images and 12198 cells. Experimental
results demonstrate that our algorithm achieves an AP_segm of 0.555, which
remarkably overtakes a benchmark by a margin of 23.6%. This study's success
opens up a new possibility of using rotated images as input for better
prediction in cell images.
