Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy

06/18/2019
by   Petru Manescu, et al.
1

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 4

page 5

page 8

page 9

05/12/2017

Deep Learning Microscopy

We demonstrate that a deep neural network can significantly improve opti...
01/07/2018

High-throughput, high-resolution Generated Adversarial Network Microscopy

We for the first time combine generated adversarial network (GAN) with w...
01/14/2020

Methodologies for Successful Segmentation of HRTEM Images via Neural Network

High throughput analysis of samples has been a topic increasingly discus...
12/12/2017

Deep learning enhanced mobile-phone microscopy

Mobile-phones have facilitated the creation of field-portable, cost-effe...
03/24/2020

Learning to Reconstruct Confocal Microscopy Stacks from Single Light Field Images

We present a novel deep learning approach to reconstruct confocal micros...
01/07/2021

Learning Guided Electron Microscopy with Active Acquisition

Single-beam scanning electron microscopes (SEM) are widely used to acqui...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.