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Global eradication of malaria depends on the development of drugs effectiveagainst the silent, yet obligate liver stage of the disease. The gold standardin drug development remains microscopic imaging of liver stage parasites in invitro cell culture models. Image analysis presents a major bottleneck in thispipeline since the parasite has significant variability in size, shape, anddensity in these models. As with other highly variable datasets, traditionalsegmentation models have poor generalizability as they rely on hand-craftedfeatures; thus, manual annotation of liver stage malaria images remainsstandard. To address this need, we develop a convolutional neural networkarchitecture that utilizes spatial dropout sampling for parasite segmentationand epistemic uncertainty estimation in images of liver stage malaria. Ourpipeline produces high-precision segmentations nearly identical to expertannotations, generalizes well on a diverse dataset of liver stage malariaparasites, and promotes independence between learned feature maps to model theuncertainty of generated predictions.

Image segmentation of liver stage malaria infection with spatial uncertainty sampling