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Investigating the Joint Amplitude and Phase Imaging of Stained Samples in Automatic Diagnosis.
Hassini, Houda; Dorizzi, Bernadette; Thellier, Marc; Klossa, Jacques; Gottesman, Yaneck.
Afiliación
  • Hassini H; Samovar, Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France.
  • Dorizzi B; TRIBVN/T-Life, 92800 Puteaux, France.
  • Thellier M; Samovar, Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France.
  • Klossa J; AP-HP, Centre National de Référence du Paludisme, 75013 Paris, France.
  • Gottesman Y; Institut Pierre-Louis d'Épidémiologie et de Santé Publique, Sorbonne Université, INSERM, 75013 Paris, France.
Sensors (Basel) ; 23(18)2023 Sep 16.
Article en En | MEDLINE | ID: mdl-37765989
ABSTRACT
The diagnosis of many diseases relies, at least on first intention, on an analysis of blood smears acquired with a microscope. However, image quality is often insufficient for the automation of such processing. A promising improvement concerns the acquisition of enriched information on samples. In particular, Quantitative Phase Imaging (QPI) techniques, which allow the digitization of the phase in complement to the intensity, are attracting growing interest. Such imaging allows the exploration of transparent objects not visible in the intensity image using the phase image only. Another direction proposes using stained images to reveal some characteristics of the cells in the intensity image; in this case, the phase information is not exploited. In this paper, we question the interest of using the bi-modal information brought by intensity and phase in a QPI acquisition when the samples are stained. We consider the problem of detecting parasitized red blood cells for diagnosing malaria from stained blood smears using a Deep Neural Network (DNN). Fourier Ptychographic Microscopy (FPM) is used as the computational microscopy framework to produce QPI images. We show that the bi-modal information enhances the detection performance by 4% compared to the intensity image only when the convolution in the DNN is implemented through a complex-based formalism. This proves that the DNN can benefit from the bi-modal enhanced information. We conjecture that these results should extend to other applications processed through QPI acquisition.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 3_ND Problema de salud: 3_malaria Asunto principal: Eritrocitos / Microscopía Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Francia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 3_ND Problema de salud: 3_malaria Asunto principal: Eritrocitos / Microscopía Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Francia
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