Reducing the Diagnostic Burden of Malaria Using Microscopy Image Analysis and Machine Learning in the Field [Poster].
Jaeger S, Silamut K, Yu H, Poostchi Mohammadabadi M, Ersoy I, Powell A, Liang Z, Hossain M, Antani SK, Palaniappan K, Maude R, Thoma GR
Annual Meeting of the American Society of Tropical Medicine & Hygiene (ASTMH), Atlanta, USA, 2016.
Abstract:
Microscopy remains the main technique for diagnosing malaria, despite the availability of Rapid Diagnostic Tests. Hundreds of millions of blood films are examined using microscopy every year for diagnosing malaria and quantifying parasite burdens. Processing this large number of slides consumes scarce resources. Microscopy technicians who read these slides in the field may be inadequately trained or overwhelmed with the volume of slides to process, leading to missed and incorrect diagnoses. To ease the burden for microscopists and improve diagnostic and quantitative accuracy, we have developed a smartphone application that can assist field microscopists in diagnosis of malaria. The software runs on a standard Android smartphone that is attached to a microscope by a low cost adapter. Images of thin-film microscope slides are acquired through the eye piece of the microscope using the smartphone’s built-in camera. The smartphone application assists microscopists in detecting parasites and estimating the parasitaemia. For each microscope field, the image processing software identifies infected and uninfected cells, and reports the parasite count per microliter of blood. The software was trained with more than 200,000 red blood cells from slides acquired at Chittagong Medical College Hospital in Bangladesh from patients with and without P.falciparum infection. These were manually annotated by an experienced professional slide reader. This is one of the largest labeled malaria slide image collections, enabling the application of new machine learning techniques such as deep learning. For each field-of-view image taken, an image processing pipeline is applied first to detect and segment cells before computing color and texture features for automatic machine classification to discriminate between infected and uninfected cells and other objects in the slide. Initial experiments show that our software correlates highly with human experts and flow cytometry.
Jaeger S, Silamut K, Yu H, Poostchi Mohammadabadi M, Ersoy I, Powell A, Liang Z, Hossain M, Antani SK, Palaniappan K, Maude R, Thoma GR. Reducing the Diagnostic Burden of Malaria Using Microscopy Image Analysis and Machine Learning in the Field [Poster].
Annual Meeting of the American Society of Tropical Medicine & Hygiene (ASTMH), Atlanta, USA, 2016.