1. Erlend Hodneland, University of Bergen Automated detection of TNT in cell images.

2. Erlend Hodneland, University of Bergen Automated detection of TNT in cell images.

3. Erlend Hodneland, University of Bergen Automated detection of TNTs(Tunnelling NanoTubes) in cell images

4. Erlend Hodneland, University of Bergen Automated detection of TNTs(Tunnelling NanoTubes) in cell images Erlend Hodneland, Arvid Lundervold, Xue-Cheng Tai, Steffen Gurke, Amin Rustom, Hans-Hermann Gerdes.

5. Erlend Hodneland, University of Bergen 3D session at fluorescence microscope Dimension : 520x688x40 Dimension : 520x688x40 Better resolution in xy plane than in z direction. Better resolution in xy plane than in z direction.

6. Erlend Hodneland, University of Bergen Two image channels The channels appear from biological stainings of sample. The stainings are photo sensible to specific wavelengths and accumulate in certain compartmens of the cells.

7. Erlend Hodneland, University of Bergen First channel displaying cell borders and TNTs

8. Erlend Hodneland, University of Bergen Gaussian noise and undesired structures

9. Erlend Hodneland, University of Bergen Video of image stack

10. Erlend Hodneland, University of Bergen Second channel displaying cell cytoplasma

11. Erlend Hodneland, University of Bergen Biological relevance of TNTs TNTs are until recently unknown cell structures. TNTs are until recently unknown cell structures. Play a role in cell to cell communication. Play a role in cell to cell communication. Transport of virus? Transport of virus? Spread of cancer? Spread of cancer? Virus moving? Cell 1Cell 2

12. Erlend Hodneland, University of Bergen Automated detection of TNTs A very challenging problem due to large variability between images. A very challenging problem due to large variability between images. The basis methods are built up around The basis methods are built up around Zerocross and Canny edgedetectors. Zerocross and Canny edgedetectors. Morphology incl. Watershed segmentation, binary filling, dilation, erosion, closing and opening. Morphology incl. Watershed segmentation, binary filling, dilation, erosion, closing and opening.

13. Erlend Hodneland, University of Bergen Morhpological operators* *Serra, J 1982, Image analysis and mathematical morphology., Academic Press.

14. Erlend Hodneland, University of Bergen Morhpological operators* *Serra, J 1982, Image analysis and mathematical morphology., Academic Press.

15. Erlend Hodneland, University of Bergen Morhpological operators* *Serra, J 1982, Image analysis and mathematical morphology., Academic Press.

16. Erlend Hodneland, University of Bergen Morhpological operators* *Serra, J 1982, Image analysis and mathematical morphology., Academic Press.

17. Erlend Hodneland, University of Bergen Morhpological operators* *Serra, J 1982, Image analysis and mathematical morphology., Academic Press.

18. Erlend Hodneland, University of Bergen Step #1 : Find cellular regions Using canny edge detector to find borders of cells. Using canny edge detector to find borders of cells. Edge detectors create lots of broken parts, we need to combine these parts. Edge detectors create lots of broken parts, we need to combine these parts.

19. Erlend Hodneland, University of Bergen Step #1 : Find cellular regions Use morphological closing and dilation to combine edges into closed regions. Use morphological closing and dilation to combine edges into closed regions. Dilation and closing

20. Erlend Hodneland, University of Bergen Step #1 : Find cellular regions Use morphological filling to fill closed regions. Use morphological filling to fill closed regions. Cells shown as white, filled regions. Cells shown as white, filled regions. Filling

21. Erlend Hodneland, University of Bergen Step #1 : Find cellular regions 3-D representation of binary cell image. 3-D representation of binary cell image. Combine All planes.

22. Erlend Hodneland, University of Bergen Step #2 Find important edges in cell border channel The first channel displays TNTs and cell borders. The first channel displays TNTs and cell borders. TNTs have low intensities compared to cell borders but they have a large gradient. TNTs have low intensities compared to cell borders but they have a large gradient.

23. Erlend Hodneland, University of Bergen Step #2 Find important edges in cell border channel Remove edges inside cells. Remove edges inside cells.

24. Erlend Hodneland, University of Bergen Watershed segmentation A segmentation procedure specially designed for images with natural minima. A segmentation procedure specially designed for images with natural minima. A reliable segmentation method, but it needs suitable minima regions as input for the region growing. A reliable segmentation method, but it needs suitable minima regions as input for the region growing. Background Cell

25. Erlend Hodneland, University of Bergen Watershed segmentation Pathwise criterion of Watershed lines W: Pathwise criterion of Watershed lines W: For all A i (a,b), min(W(a,b)) ≥ min(A i (a,b)) ”Moving on the top of the hill” a b Region 1 Region 2 Region 3 min(Ai(a,b)) min(W(a,b))

26. Erlend Hodneland, University of Bergen Watershed segmentation The minima seeding regions are extremely important and decide where the watershed lines will appear. The minima seeding regions are extremely important and decide where the watershed lines will appear. Minima regions Minima imposed on image

27. Erlend Hodneland, University of Bergen Watershed segmentation Results improve when the minima seeding regions are close to the crest of the desired structures. Results improve when the minima seeding regions are close to the crest of the desired structures. 1 2 3 4 5 6 7 Watershed image, {1,2 … 7} The boundaries of cells

28. Erlend Hodneland, University of Bergen Watershed segmentation Results improve when the minima seeding regions are close to the crest of the desired structures. Results improve when the minima seeding regions are close to the crest of the desired structures. 1 2 3 4 5 6 7 Watershed image, {1,2 … 7} The boundaries of cells

29. Erlend Hodneland, University of Bergen Step #3 Watershed segmentation to find crest of structures from edges TNTs are thin and narrow, approximately 3- 4 pixles wide (50- 200nm). TNTs are thin and narrow, approximately 3- 4 pixles wide (50- 200nm). 1 1 TNT

30. Erlend Hodneland, University of Bergen Step #3 Watershed segmentation to find crest of structures from edges Problem : The structures from the edge image are not always continuous and they are not marking the crest of the structure. Problem : The structures from the edge image are not always continuous and they are not marking the crest of the structure. Solution : Use watershed segmentation to create connected lines on the crest of the high intensity structures. Solution : Use watershed segmentation to create connected lines on the crest of the high intensity structures.

31. Erlend Hodneland, University of Bergen Step #3 Watershed segmentation to find crest of structures from edges Problem : The structures from the edge image are not always continuous and they are not marking the crest of the structure. Problem : The structures from the edge image are not always continuous and they are not marking the crest of the structure. 1 TNT 1

32. Erlend Hodneland, University of Bergen Step #3 Watershed segmentation to find crest of structures from edges Important: TNTs can cross several planes. Important: TNTs can cross several planes. Therefore we use a projection in 3-D  2-D to include the whole TNT. Therefore we use a projection in 3-D  2-D to include the whole TNT. All projections are ranging over the same planes as the structure we investigate. All projections are ranging over the same planes as the structure we investigate. Cell 2 x y z Cell 1 TNT 10 20

33. Erlend Hodneland, University of Bergen Step #3 Watershed segmentation to find crest of structures from edges Plane 10 Plane 11 Plane 12 Plane 13

34. Erlend Hodneland, University of Bergen Step #3 Watershed segmentation to find crest of structures from edges Using the maximum projection of the structure from the edge image to take advantage of 3-D information. Using the maximum projection of the structure from the edge image to take advantage of 3-D information. 1 TNT Maximum projection and closing

35. Erlend Hodneland, University of Bergen Step #3 Watershed segmentation to find crest of structures from edges We use information from the segmentation of cells to construct minima regions to seed the Watershed segmentation. We use information from the segmentation of cells to construct minima regions to seed the Watershed segmentation. Background Cells TNT

36. Erlend Hodneland, University of Bergen Step #3 Watershed segmentation to find crest of structures from edges We use information from the segmentation of cells to construct minima regions to seed the Watershed segmentation. We use information from the segmentation of cells to construct minima regions to seed the Watershed segmentation. TNT Morphological opening Impose (1) on (2) 1 2 Minima regions

37. Erlend Hodneland, University of Bergen Step #3 Watershed segmentation to find crest of structures from edges For Watershed we use the sum projection of the image to take advantage of 3-D information and for Gaussian noise supression. For Watershed we use the sum projection of the image to take advantage of 3-D information and for Gaussian noise supression. TNT Sum projection Image stack

38. Erlend Hodneland, University of Bergen Step #3 Watershed segmentation to find crest of structures from edges Using Watershed segmentation to achieve a connected line on the crest of the structure from the edge image. Using Watershed segmentation to achieve a connected line on the crest of the structure from the edge image. TNT Watershed segmentation TNT Minima regions

39. Erlend Hodneland, University of Bergen Step #4 Removal of false TNT candidates We end up with numerous TNT candidates, some false and some true. We end up with numerous TNT candidates, some false and some true. TNT candidates Watershed segmentation

40. Erlend Hodneland, University of Bergen Step #4 Removal of false TNT candidates Each TNT candidate must undergo an evaluation of correctedness. Remove candidates Each TNT candidate must undergo an evaluation of correctedness. Remove candidates having low intensities compared to their surroundings. having low intensities compared to their surroundings. not crossing between two cells. not crossing between two cells. not beeing straigth lines using hough transformation. not beeing straigth lines using hough transformation. crossing at the nearest distance of the cells. crossing at the nearest distance of the cells. We are left with ”true” TNT structures after the exclusion evaluation. We are left with ”true” TNT structures after the exclusion evaluation.

41. Erlend Hodneland, University of Bergen Results We have employed our algorithm to 51 3-D image stacks: We have employed our algorithm to 51 3-D image stacks: Success rate 67% Success rate 67% False positive 50% False positive 50% False negative 33% False negative 33% compared to manual counting. The high number of false positive TNTs is mostly due to large image variations and irregularities of the cells. The high number of false positive TNTs is mostly due to large image variations and irregularities of the cells.

42. Erlend Hodneland, University of Bergen Results Large irregularites. Large irregularites. Main reason for false positive or false negative TNTs. Main reason for false positive or false negative TNTs.

43. Erlend Hodneland, University of Bergen Results Large irregularites. Large irregularites. Main reason for false positive or false negative TNTs. Main reason for false positive or false negative TNTs.

44. Erlend Hodneland, University of Bergen Results Nano experiments to grow the cells on pre- defined matrices. Nano experiments to grow the cells on pre- defined matrices. This will improve the automated detection. This will improve the automated detection.

45. Erlend Hodneland, University of Bergen Conclusion We have developed an automated method for counting TNTs in cell images. We have developed an automated method for counting TNTs in cell images. The method is essentially based on existing image processing techniques like edge-detectors, watershed segmentation and morphological operators. The method is essentially based on existing image processing techniques like edge-detectors, watershed segmentation and morphological operators. We report a success rate of 67% compared to manual counting. We report a success rate of 67% compared to manual counting.