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Automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Science for Life Laboratory, SciLifeLab. (Quantitative Microscopy)ORCID-id: 0000-0002-6699-4015
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Science for Life Laboratory, SciLifeLab. (Quantitative Microscopy)
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.ORCID-id: 0000-0002-1636-3469
2016 (engelsk)Konferansepaper, Poster (with or without abstract) (Annet vitenskapelig)
Abstract [en]

Aim

This work describes a proof-of-principle study within the Exchange of Diagnostic Images in Networks (ExDIN) project, for automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections. The idea was to mimic the manual grading process: “The assessment is carried out on invasive cancer within the area with the highest number of Ki67-positive cancer cell nuclei/area (hot spot), containing at least 200 cells.”

Method

  • Color deconvolution to separate the image into brown and blue channels.

  • Extract the 10 subsampled tiles (size corresponding to ~200 cells) with the highest values for pre-defined texture and color features.

  • Analyze these tiles in full resolution and compute the maximum positivity (defined as area of positive cells in relation to total cell area, rather than number of cells, since that will speed up the computations and avoid introducing errors due to over- or under segmentation of connected objects).

     

     

     

     

     

     

     

     

     

     

     

     

     

Figure 1. Illustration of the procedure. Hot spot candidates are extracted from low resolution tiles. Then the final hot spot is selected among the corresponding full resolution versions.

The results show good correlation to manual estimates and the procedure takes ~4 minutes/slide.

Future improvements

  • Rules and features defined using machine learning based on training samples given by pathologists.

  • User interface where suggested regions can be deselected manually.

sted, utgiver, år, opplag, sider
2016.
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-309606OAI: oai:DiVA.org:uu-309606DiVA, id: diva2:1052279
Konferanse
4th Nordic Symposium on Digital Pathology
Prosjekter
ExDINTilgjengelig fra: 2016-12-06 Laget: 2016-12-06 Sist oppdatert: 2016-12-21

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Ranefall, PetterWählby, CarolinaBengtsson, Ewert

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