Computational Imaging and Precision Medicine
Challenges and Opportunities
Posted 30th September 2016 by Jane Williams
The digitization of tissue glass slides is clearly opening up exciting opportunities as well as challenges to the world of computational imaging scientists. It is clear that while computational imaging can clearly play a role in better quantitative characterization of disease and precision medicine, there still remain a number of substantial technical and computational challenges that need to be overcome before computer assisted image analysis of digital pathology can become part of the routine clinical diagnostic workflow.
The challenges
On the technical side, one of the main challenges in the computational interpretation of digital slide images has to do with colour variations in the tissue induced by differences in slide preparation, staining, and even whole slide scanners. Clearly decision support algorithms that aim to work on digital pathology images will have to contend with and be resilient to these variations.
A second technical challenge has to with the fact that most whole slide digital scanners are only able to generate 2D planar images of the slides and unable to capture the z-axis depth information. This information is available to pathologists on their microscopes and is useful for a number of tasks such as in confirming the presence of mitotic figures. However some whole slide scanner manufacturers are already beginning to recognize the importance of accommodating the z-stack and we can anticipate 3d whole slide scanners soon. The availability of a new dimension to accompany the dense planar data will no doubt further put pressure on the algorithmic scientists to develop more intelligent and efficient approaches for detecting, segmenting, analyzing, and interrogating 3d stacks of digitized slide images.
This issue of computational complexity will become further exacerbated with the spread and availability of multi-spectral imaging cameras for investigation of multiple different tissue analytes, where each tissue section could be imaged at multiple different wavelengths and hence comprise hundreds of accompanying images. Approaches like deep learning which attempt to perform unsupervised feature analysis and discovery will clearly need to be operating at much higher levels of computational efficiency and in conjunction with high performance computing and GPU clusters (Ali and Madabhushi, 2011) to deal with the impending data deluge.
The opportunities
Despite the aforementioned challenges, the opportunities opened up by computational imaging of digital pathology are tantalizing. In spite of the reluctance thus far by the regulatory agencies to grant approval to whole slide scanned images for use for primary diagnosis, it is clear that the use of computer aided analysis with digital pathology will be part of clinical decision making in the near future. Apart from substantially aiding the pathologists in decision making, the use of computational imaging tools could enable the creation of digital imaging based companion diagnostic assays that could allow for improved disease risk characterization (Basavanhally et al., 2011b; Lewis et al., 2014).
Unlike expensive molecular based assays that involve destroying the tissue and invariably capture genomic or proteomic measurements from a small part of the tumor, these digital imaging based companion diagnostic tests could be offered for a fraction of the price, could enable characterization of disease heterogeneity across the entire landscape of the tissue section, and would not need physical shipping of the tissue samples.
For the biomedical image computing, machine learning, and bioinformatics scientists, the aforementioned challenges will present new and exciting opportunities for developing new feature analysis and machine learning opportunities. One very interesting image computing research area that digital pathology opens up is the ability to combine traditional handcrafted feature approaches with deep learning methodologies, thereby taking advantage of domain knowledge while also enabling the classifier to discover new features. Another exciting research avenue will be in the development of new data fusion algorithms for combining radiologic, histologic, and molecular measurements for improved disease characterization.
Computational imaging advances for digital pathology will finally begin to make pathology more quantitative, a field that has thus far significantly lagged behind radiology in this regard. By all indications, this transformation from qualitative to quantitative pathology is in the not too distant future.
Anant Madabhushi is Professor & Director at the Centre for Computational Imaging and Personalized Diagnostics. He will be making the keynote address at the 2nd Digital Pathology Congress, USA on the ways digital pathology is bridging radiology and molecular ‘omics’ worlds.
References
Ali, S., Madabhushi, A., 2011. Graphical processing unit implementation of an integrated shape-based active contour: Application to digital pathology. J. Pathol. Inform. 2, S13. doi:10.4103/2153-3539.92029
Basavanhally, A., Feldman, M., Shih, N., Mies, C., Tomaszewski, J., Ganesan, S., Madabhushi, A., 2011b. Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX. J. Pathol. Inform. 2, S1. doi:10.4103/2153-3539.92027
Lewis, J.S., Ali, S., Luo, J., Thorstad, W.L., Madabhushi, A., 2014. A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma. Am. J. Surg. Pathol. 38, 128–137. doi:10.1097/PAS.0000000000000086
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