Segmentation of cell nuclei in heterogeneous microscopy images: A reshapable templates approach

Histological coherent Raman imaging: a prognostic review

Histopathology plays a central role in diagnosis of many diseases including solid cancers. Efforts are underway to transform this subjective art to an objective and quantitative science. Coherent Raman imaging (CRI), a label-free imaging modality with sub-cellular spatial resolution and molecule-specific contrast possesses characteristics which could support the qualitative-to-quantitative transition of histopathology. In this work we briefly survey major themes related to modernization of histopathology, review applications of CRI to histopathology and, finally, discuss potential roles for CRI in the transformation of histopathology that is already underway.

High-throughput image analysis in the diagnosis of papillary thyroid carcinoma

BACKGROUND

Thyroid cancer is the most common endocrine cancer, and its incidence has been increasing worldwide in the past decades. The increasing demand in medicine for rapid and accurate diagnosis enabled the application of digital imaging analysis in order to increase workflow efficiency and accurate analyses. The present study aimed to automatically differentiate papillary thyroid carcinoma from normal thyroid cells using high-throughput image analysis.

METHODS

Images of cellular specimens were taken with a digital camera and were subsequently analyzed. Other software was used for machine-learning-based cellular diagnostics.

RESULTS

The two different classes were correctly identified with high sensitivity and specificity.

CONCLUSION

The data created offers great potential for an automated diagnosis. Diagn. Cytopathol. 2016;44:574-577. © 2016 Wiley Periodicals, Inc.