A Smartphone-Based Cell Segmentation to Support Nasal Cytology

Abstract

Rhinology studies the anatomy, physiology, and diseases affecting the nasal region—one of the most modern techniques to diagnose these diseases is nasal cytology, which involves microscopic analysis of the cells contained in the nasal mucosa. The standard clinical protocol regulates the compilation of the rhino-cytogram by observing, for each slide, at least 50 fields under an optical microscope to evaluate the cell population and search for cells important for diagnosis. The time and effort required for the specialist to analyze a slide are significant. In this paper, we present a smartphones-based system to support cell segmentation on images acquired directly from the microscope. Then, the specialist can analyze the cells and the other elements extracted directly or, alternatively, he can send them to Rhino-cyt, a server system recently presented in the literature, that also performs the automatic cell classification, giving back the final rhinocytogram. This way he significantly reduces the time for diagnosing. The system crops cells with sensitivity = 0.96, which is satisfactory because it shows that cells are not overlooked as false negatives are few, and therefore largely sufficient to support the specialist effectively. The use of traditional image processing techniques to preprocess the images also makes the process sustainable from the computational point of view for medium–low end architectures and is battery-efficient on a mobile phone.

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

“…Therefore, high expectations are placed in the use of such techniques also for the improvement of health care and clinical practice [ 31 , 32 , 33 , 34 , 35 , 36 ]. Furthermore, numerous portable devices for personal and frequent monitoring of cardiac activity, such as Kardia [ 37 ], D-hearth [ 38 ], and eKuore [ 39 ], are spreading.…”