  Fast accurate diagnosis of malaria is still a global health challenge for
which automated digital-pathology approaches could provide scalable solutions
amenable to be deployed in low-to-middle income countries. Here we address the
problem of Extended Depth-of-Field (EDoF) in thick blood film microscopy for
rapid automated malaria diagnosis. High magnification oil-objectives (100x)
with large numerical aperture are usually preferred to resolve the fine
structural details that help separate true parasites from distractors. However,
such objectives have a very limited depth-of-field requiring the acquisition of
a series of images at different focal planes per field of view (FOV). Current
EDoF techniques based on multi-scale decompositions are time consuming and
therefore not suited for high-throughput analysis of specimens. To overcome
this challenge, we developed a new deep learning method based on Convolutional
Neural Networks (EDoF-CNN) that is able to rapidly perform the extended
depth-of-field while also enhancing the spatial resolution of the resulting
fused image. We evaluated our approach using simulated low-resolution z-stacks
from Giemsa-stained thick blood smears from patients presenting with Plasmodium
falciparum malaria. The EDoF-CNN allows speed-up of our digital-pathology
acquisition platform and significantly improves the quality of the EDoF
compared to the traditional multi-scaled approaches when applied to lower
resolution stacks corresponding to acquisitions with fewer focal planes, large
camera pixel binning or lower magnification objectives (larger FOV). We use the
parasite detection accuracy of a deep learning model on the EDoFs as a
concrete, task-specific measure of performance of this approach.
