Computational pathology

Computational Pathology

Developing image analytics that can quantify pathogenesis in a high-throughput, bias-free and robust way


In histopathology, pathologists assess patient biopsies and tissue resections to study the presence and/or grade of a disease, but also for selecting personalized treatment and monitoring. With respect to other diagnostic technologies, tissue analysis is more invasive, but at the same time provides much higher resolution.

Currently, pathologists assess tissues under a microscope, leading to diagnoses affected by subjective judgment and intra- and inter-observer variability. This is due to the difficulty of the process. The staining intensity in the tissue, as well as the morphological and cellular architecture indicate cancer and many diseases.

However, disease susceptibility and progression is a complex, multifactorial molecular process. Diseases such as cancer exhibit tissue and cellular heterogeneities, which impedes 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 to bring hospitals into the digital era are using bright-field and fluorescence scanners to convert glass slides of tissue specimens and needle biopsies into virtual microscopy images of very high quality, enabling digital image analysis.

Our focus

At IBM Research in Zurich, we are focusing on the analysis of digitized histopathology and molecular expression images, as well as cytology images. Imaging tissue specimens is a powerful tool to extract quantitative metrics of phenotypic properties while preserving the morphology and spatial relationship of the tissue micro-environment.

Novel staining technologies such as immuno­histo­chemistry (IHC) and in situ hybridization (ISH) further improve 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 for selecting treatment 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 selected features in stained tissue samples is ambiguous, tedious and time-consuming, and therefore prone to technician and clerical errors. This in turn leads to intra- and interobserver variability and low throughput.

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.

Value proposition


Cost savings

$17.8 million

Projected 5-year total cost savings for a large, academic-based healthcare organization upon fully implementing a DP system.

$3 million

Annual savings of FEDEX costs for a diagnostics services provider with a DP system that analyzes 4,000–5,000 slides daily.




The projects conducted at the Zurich lab included prostate cancer stratification, breast cancer tumor proliferation estimation, colorectal cancer metastasis study, tissue heterogeneity quantification, protein signature quantification, stained image quality metric and sensitivity to staining parameters estimation