  Trichomoniasis is a common infectious disease with high incidence caused by
the parasite Trichomonas vaginalis, increasing the risk of getting HIV in
humans if left untreated. Automated detection of Trichomonas vaginalis from
microscopic images can provide vital information for the diagnosis of
trichomoniasis. However, accurate Trichomonas vaginalis segmentation (TVS) is a
challenging task due to the high appearance similarity between the Trichomonas
and other cells (e.g., leukocyte), the large appearance variation caused by
their motility, and, most importantly, the lack of large-scale annotated data
for deep model training. To address these challenges, we elaborately collected
the first large-scale Microscopic Image dataset of Trichomonas Vaginalis, named
TVMI3K, which consists of 3,158 images covering Trichomonas of various
appearances in diverse backgrounds, with high-quality annotations including
object-level mask labels, object boundaries, and challenging attributes.
Besides, we propose a simple yet effective baseline, termed TVNet, to
automatically segment Trichomonas from microscopic images, including
high-resolution fusion and foreground-background attention modules. Extensive
experiments demonstrate that our model achieves superior segmentation
performance and outperforms various cutting-edge object detection models both
quantitatively and qualitatively, making it a promising framework to promote
future research in TVS tasks. The dataset and results will be publicly
available at: https://github.com/CellRecog/cellRecog.
