Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood cells infected by the malaria parasite Plasmodium falciparum at trophozoite or schizont stage. Unlike previous efforts in this area, this study uses quantitative phase images of unstained cells. Erythrocytes are automatically segmented using thresholds of optical phase and refocused to enable quantitative comparison of phase images. Refocused images are analyzed to extract 23 morphological descriptors based on the phase information. While all individual descriptors are highly statistically different between infected and uninfected cells, each descriptor does not enable separation of populations at a level satisfactory for clinical utility. To improve the diagnostic capacity, we applied various machine learning techniques, including linear discriminant classification (LDC), logistic regression (LR), and k-nearest neighbor classification (NNC), to formulate algorithms that combine all of the calculated physical parameters to distinguish cells more effectively. Results show that LDC provides the highest accuracy of up to 99.7% in detecting schizont stage infected cells compared to uninfected RBCs. NNC showed slightly better accuracy (99.5%) than either LDC (99.0%) or LR (99.1%) for discriminating late trophozoites from uninfected RBCs. However, for early trophozoites, LDC produced the best accuracy of 98%. Discrimination of infection stage was less accurate, producing high specificity (99.8%) but only 45.0%-66.8% sensitivity with early trophozoites most often mistaken for late trophozoite or schizont stage and late trophozoite and schizont stage most often confused for each other. Overall, this methodology points to a significant clinical potential of using quantitative phase imaging to detect and stage malaria infection without staining or expert analysis.
Quantitative phase spectroscopy is presented as a novel method of measuring the wavelength-dependent refractive index of microscopic volumes. Light from a broadband source is filtered to an ~5 nm bandwidth and rapidly tuned across the visible spectrum in 1 nm increments by an acousto-optic tunable filter (AOTF). Quantitative phase images of semitransparent samples are recovered at each wavelength using off-axis interferometry and are processed to recover relative and absolute dispersion measurements. We demonstrate the utility of this approach by (i) spectrally averaging phase images to reduce coherent noise, (ii) measuring absorptive and dispersive features in microspheres, and (iii) quantifying bulk hemoglobin concentrations by absolute refractive index measurements. Considerations of using low coherence illumination and the extension of spectral techniques in quantitative phase measurements are discussed.
Background & Aims-Patients with Barrett's esophagus (BE) show increased risk for developing esophageal adenocarcinoma and are routinely examined using upper endoscopy with biopsy to search for neoplastic changes. Angle-resolved low coherence interferometry (a/LCI) uses in vivo depth-resolved nuclear morphology measurements to detect dysplasia. We assessed the clinical utility of a/LCI in the endoscopic surveillance of BE patients.
We present a phase-shifting interferometric technique for imaging live biological cells in growth media, while optimizing spatial resolution and enabling potential real-time measurement capabilities. The technique uses slightly-off-axis interferometry which requires less detector bandwidth than traditional off-axis interferometry and fewer measurements than traditional on-axis interferometry. Experimental and theoretical comparisons between the proposed method and these traditional interferometric approaches are given. The method is experimentally demonstrated via phase microscopy of live human skin cancer cells.
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