Re: TCGA / Microscopy Imaging Use Case

Wow, that is an interesting use case. Maybe this may calls for a better 
definition of what we mean by 'spatial data'?

I remember the time when geographers started switching from using the 
adjective 'geographic' to using 'spatial', implying a broadening of 
scope and a higher relevance. But still the actual topics were 
macroscopic objects, things that you can plot on a map. And the 
reference systems still are earth based.

Taken literally, 'spatial' covers a lot more than 'geographic'. It 
includes concepts from all scales, from quantum particles to the 
universe itself. Are we ready to take on all these scales?

Related to the issue of the scope of scale is the relationship between 
space and time. I think that on the human/macroscopic/geographical level 
space and time can be kept separate, in the sense that a model or 
ontology for space does not really need time concepts, and vice versa. 
But it could well be that such a separation is not possible for very 
small things (like elemental particles) and very big things (like 
galaxies). On such levels time and space tend to be more entangled.

Greetings,
Frans

On 2015-03-03 21:52, Erich Bremer wrote:
>
>  Studying the morphology of disease at the cellular and sub-cellular 
> levels using high resolution tissue images is extremely important to 
> help understand the nature of various cancers. The Cancer Genome Atlas 
> (TCGA) (http://cancergenome.nih.gov/) contains over 32,000 
> de-identified whole-slide microscopy images (WSI) of over two dozen 
> cancer types. These images can contain between 100K-1M nuclei each.  
> Biomedical informatics researcher have developed (and continue to 
> develop) software to automatically segment nuclei for study.  The 
> spatial features of each nucleus and groups of nuclei as it relates to 
> other nuclei combined with other linked data such as other 
> morphological features (crypts, ducts, etc) and/or patient lab results 
> are used in analyzing and categorizing tissues and patients into 
> groups and in comparing such groupings to understand disease 
> mechanisms in a particular cancer type as well as across cancer types.
>
> Representing nuclear segmentations is often done with binary masks or 
> through polygon representations (e.g., the use of Well Known Text 
> (WKT) representations) and also by leveraging work from the Geospatial 
> community.  However, in the case of nuclear segmentations, coordinate 
> systems are 2D & 3D Cartesian based.  Although the majority of work is 
> this area is 2D-based, a growing segment of microscopy is also 
> 3D-based as the technology develops and become more sophisticated.  As 
> living tissue can change over time through growth, infection, cancer, 
> damage, etc, (as well as its associated organism’s various properties) 
> it is important that spatial locations of features such as nuclear 
> segmentation be also represented in a temporal aspect for proper 
> comparisons.
>
> Samples of TCGA WSI data can be viewed at: 
> http://cancer.digitalslidearchive.net
>
>
> -- 
> ==========================================================
> Erich Bremer, M.Sc.
> Director for Cyberinfrastructure
> Health Sciences Division of Applied Informatics
> Stony Brook Medicine
> Tel. : 1-631-444-3560
> Fax  : 1-631-444-8873
> Cell : 1-631-681-6228
> erich.bremer@stonybrook.edu <mailto:erich.bremer@stonybrook.edu>
> Office Location/Mailing Address
> HSC, L3: Room 119
> Stony Brook, NY 11794-8330
>


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