3D Processing and Analysis with ImageJ
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1 3D Processing and Analysis with ImageJ Rapid development of technologies yielding multidimensional data LSCM / Video Electron tomography IRM / CT scan Thomas Boudier, Associate Professor, Cellular Imaging Center, University Pierre et Marie Curie, Paris, France. thomas.boudier@snv.jussieu.fr 06/11/2008 ImageJ Conf - T. Boudier 1 06/11/2008 ImageJ Conf - T. Boudier 2 Electron tomography Series of tilted projections Reconstruct original volume Set of 3D projections -60 to 60 Melanosomes Hurbain et al. Electron tomography of early melanosomes PNAS D = τ/cos(α) α 06/11/2008 ImageJ Conf - T. Boudier 3 06/11/2008 ImageJ Conf - T. Boudier 4 Reconstruction 3D volume Melanosomes Hurbain et al. Electron tomography of early melanosomes PNAS Modelisation Drawing of 3D structures Melanosomes Hurbain et al. Electron tomography of early melanosomes PNAS /11/2008 ImageJ Conf - T. Boudier 5 06/11/2008 ImageJ Conf - T. Boudier 6 Thomas Boudier - UPMC 1
2 Reading data Steps of analysis : Reading data Visualization Processing Reduce noise Enhance objects Segmentation Analysis 3D data : X-Y + Z Stack of 2D images 4D data : 3D data + time 5D data : 3D data + time + channel 06/11/2008 ImageJ Conf - T. Boudier 7 06/11/2008 ImageJ Conf - T. Boudier 8 Reading data A set of 2D files Import image sequence One file storing all images Tiff Proprietary formats Stk, lif, zvi, dm3, 2D visualisation Only one slice is displayed Adjust brightness/contrast Normalize values for all slices Thickness increase in tomography Bleaching in fluo Use LOCI Bio-Formats plugin 06/11/2008 ImageJ Conf - T. Boudier 9 06/11/2008 ImageJ Conf - T. Boudier 10 2D visualisation Reslicing Coronal, horizontal and sagittal sections Different spacing XY and Z Interpolation Isotropic data Electron tomography Volume Viewer plugin Interactive crosssections Volume rendering Volume Slicer Macro for making animations 3D visualization 06/11/2008 ImageJ Conf - T. Boudier 11 06/11/2008 ImageJ Conf - T. Boudier 12 Thomas Boudier - UPMC 2
3 3D visualization ImageJ 3D Viewer Volume and surface rendering Multiple data Registration Transparencies 4D data 2D filtering slice by slice 3D filtering A sphere of a given radius Usual filters : mean, median, min, max, Time-consuming Use of JNI or multi-threading... 06/11/2008 ImageJ Conf - T. Boudier 13 06/11/2008 ImageJ Conf - T. Boudier 14 3D Processing Noise reduction Objects same location in consecutive slices Not noise Common filters : 2D vs 3D median processing Radius = 2 Mean, gaussian Median «Enhanced» filters : sigma or shift Anisotropic filtering 06/11/2008 ImageJ Conf - T. Boudier 16 06/11/2008 ImageJ Conf - T. Boudier 15 Enhance objects «Hard» smoothing to homogeneize values inside the objects Bright spots detection Tophat filtering Minimum filtering (supress bright spots) Maximum filtering (compute background) Difference between original and background spots 06/11/2008 ImageJ Conf - T. Boudier 17 06/11/2008 ImageJ Conf - T. Boudier 18 Thomas Boudier - UPMC 3
4 2D vs 3D top hat processing Radius = 7 Detection of 3D objects 3D objects may be quite complex (ex: golgi) Manual segmentation Set of ROI Segmentation Editor Manual binarization Threshold each slices independently 06/11/2008 ImageJ Conf - T. Boudier 19 Only 2D objects 06/11/2008 ImageJ Conf - T. Boudier 20 Segmentation Editor Draw structure on each slice Display 3D structure LiveWire Tool see TrackEM2 Manual binarization Create a 3D object by connecting 2D crosssections 3D Object Counter One treshold for all slices 06/11/2008 ImageJ Conf - T. Boudier 21 06/11/2008 ImageJ Conf - T. Boudier 22 Mathematical morphology Two basic operations Erosion and dilatation Improve binarization Smooth objects (close) Separate objects (open, watershed) Geometrical features Distances Intensity features Surface analysis Granulometry Fill holes inside objects 06/11/2008 ImageJ Conf - T. Boudier 23 06/11/2008 ImageJ Conf - T. Boudier 24 Thomas Boudier - UPMC 4
5 Geometrical features Volume, surface, center Can be computed from 2D slices Feret's diameter Needs 3D computation Ellipsoid fitting Main axes Main and median elongation 06/11/2008 ImageJ Conf - T. Boudier 25 06/11/2008 ImageJ Conf - T. Boudier 26 3D distances Center to center Center to border Border to border Distances along a direction Intensity features Integrated density Mass centers Statistical values Mean, variance, min, max Intensity distribution 06/11/2008 ImageJ Conf - T. Boudier 27 06/11/2008 ImageJ Conf - T. Boudier 28 Surface analysis Curvatures computation Complex mathematics Granulometry Series of opening anf closing Objects sizes analysis Distribution of distances between objects 06/11/2008 ImageJ Conf - T. Boudier 29 06/11/2008 ImageJ Conf - T. Boudier 30 Thomas Boudier - UPMC 5
6 Example : 3D F.I.S.H Intergenic distances Interaction with CTs Example : multi F.I.S.H 7 genes with colocalization 06/11/2008 ImageJ Conf - T. Boudier 31 06/11/2008 ImageJ Conf - T. Boudier 32 Example : Example : Spindle positioning in ovocytes Spindle positioning in ovocytes Compute possible poles Check if spindle moves towards closest pole K. Lee, M.H. Verlhac 06/11/2008 ImageJ Conf - T. Boudier 33 06/11/2008 ImageJ Conf - T. Boudier 34 Example : Spindle positioning in ovocytes Conclusion Growing multidimensional data 3D visualization is not more the big issue 3D Processing is related to 2D processing 2D filtering slice by slice may be an alternative K. Lee, M.H. Verlhac may be a bit more complex than 2D analysis However biology is mainly (only?) 3D (4D?) 06/11/2008 ImageJ Conf - T. Boudier 35 06/11/2008 ImageJ Conf - T. Boudier 36 Thomas Boudier - UPMC 6
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