  Global eradication of malaria depends on the development of drugs effective
against the silent, yet obligate liver stage of the disease. The gold standard
in drug development remains microscopic imaging of liver stage parasites in in
vitro cell culture models. Image analysis presents a major bottleneck in this
pipeline since the parasite has significant variability in size, shape, and
density in these models. As with other highly variable datasets, traditional
segmentation models have poor generalizability as they rely on hand-crafted
features; thus, manual annotation of liver stage malaria images remains
standard. To address this need, we develop a convolutional neural network
architecture that utilizes spatial dropout sampling for parasite segmentation
and epistemic uncertainty estimation in images of liver stage malaria. Our
pipeline produces high-precision segmentations nearly identical to expert
annotations, generalizes well on a diverse dataset of liver stage malaria
parasites, and promotes independence between learned feature maps to model the
uncertainty of generated predictions.
