Histopathology
In histopathology, pathologists assess patient biopsies and tissue resections in order to study the presence and or the grade of a disease, but also for personalized treatment selection and monitoring purposes. With respect to other diagnostic technologies, tissue analysis is more invasive but at the same time provides much higher resolution, as shown in the image below.
Currently, pathologists assess tissues under a microscope, leading to diagnosis affected by subjective judgment and intra- and inter-observer variability. This is due to the difficulty of the process. In the tissue, the staining intensity, the morphological and cellular architecture define cancer and many diseases.
However, disease susceptibility and progression is a complex, multifactorial molecular process. Diseases such as cancer exhibit tissue and cellular heterogeneities, impeding the differentiation between different stages or types of cell formations.
At the same time, the procedure is time consuming and low-throughput, strained by the number of tissue samples generated per day.
Emerging initiatives in turning hospitals to the digital era use bright-field and fluorescence scanners to convert glass slides of tissue specimens and needle biopsies to virtual microscopy images of very high quality, enabling digital image analysis.
Contact the expert
Maria Gabrani
IBM Research scientist
Medical imaging for diagnostic purposes
At IBM Research – Zurich, we are focusing on the analysis of digitized histopathology and molecular expression images, as well as cytology images. Imaging of tissue specimens is a powerful tool in extracting quantitative metrics of phenotypic properties while preserving the morphology and spatial relationship of the tissue microenvironment.
Novel staining technologies like immunohistochemistry (IHC) and in situ hybridization (ISH) further empower the evidencing of molecular expression patterns by means of multicolor visualization.
Such techniques are thus commonly used for predicting disease susceptibility and stratification as well as treatment selection and monitoring. However, translating molecular expression imaging into direct health benefits has been slow.
Two major factors contribute to that. On the one hand, disease susceptibility and progression is a complex, multifactorial molecular process. The tissue and cell heterogeneity exhibited by diseases such as cancer occur most prominently between inflammatory response and malignant cell transition.
On the other hand, the relative quantification of the stained tissue selected features is ambiguous, tedious and thus time consuming and prone to clerical error, leading to intra- and interobserver variability and low throughput.
At IBM Research – Zurich we are developing advanced image analytics to address both the above limitations. Our aim is to transform the analysis of stained tissue images into a high-throughput, robust, quantitative and data-driven science.
Publications
[1] E. Zerhouni et al. "A computational framework for disease grading using protein signatures"
IEEE ISBI 2016 (accepted).
[2] E. Zerhouni, B. Prisacari, Q. Zhong, P. Wild, M. Gabrani,
"Deciphering protein signatures using color, morphological, and topological analysis of immunohistochemically stained human tissues”
SPIE Medical Imaging 2016.
[3] Q. Zhong, M. Gabrani, T. Guo, P.J. Schueffler, R. Aebersold, H. Moch, P.J. Wild,
"Computational profiling of heterogeneity reveals high concordance between morphology- and proteomics-based methods inprostate cancer"
DGP 2015, Frankfurt, Germany, May 28-31, 2015.
[4] E. Zerhouni, B. Prisacari, Q. Zhong, P. Wild, M. Gabrani,
"Big Data Takes on Prostate Cancer"
ERCIM News 2015.