Automated MR Image Analysis Pipelines

Transcription

1 Automated MR Image Analysis Pipelines Andy Simmons Centre for Neuroimaging Sciences, Kings College London Institute of Psychiatry. NIHR Biomedical Research Centre for Mental Health at IoP & SLAM. Neuroimaging Department, South London and Maudsley NHS Foundation Trust.

2 Overview Introduction Common pipeline tasks To GUI or not to GUI? Common pipelines Quality control Image databasing Conclusions Links

3 What is a Pipeline? A series of image analysis steps where the output from one step becomes the input to the next step Packaged by a group of programmers Command line or GUI driven

4 Why Use a Pipeline? Convenient way of analysing data Allows integration of components from multiple packages Facilitates packaging of highly CPU intensive processes Allows analysis of large (& not so large) datasets

5 Common Pipelines BrainVisa CIVET Freesurfer FSL LONI pipeline SPM components

6 Hardware & Operating Systems Range of operating systems» Windows» Macs» Unix» Linux Range of hardware» Desktops» Laptops» Clusters» Supercomputers

7 Common Pipeline Tasks Uniformity correction Segmentation/classification of GM, WM and CSF Registration of multiple images Talairach/MNI space transformation Cortical thickness Identification of gyri White matter hyperintensity segmentation Image display

8 Uniformity Correction Also known as inhomogeneity correction, bias field correction MRI RF (radio-frequency field) inhomogeneity causes intensity variations across image Causes problems for segmentation Needs to be corrected before or during segmentation Becomes more common and problematic at high field strengths

9 Uniformity Correction

10 Uniformity Correction

11 Brain Extraction Isolate the brain/csf by removing muscle, fat, bone, eyes etc Typically takes from 1 to 60 minutes per brain Accuracy crucial for subsequent analysis steps Small imperfections often don t impact on subsequent analysis

12 Brain Extraction FSL BET Accurate, robust and automated» Used for a range of slice thicknesses» Used for a range of MRI sequences» Also finds exterior skull surface» 1 minute processing time Uses a tesselated surface model» Fits model to the brain surface

13 Brain Extraction FSL BET

14 Brain Extraction FSL BET 2 Initial mesh fit to brain surface as for BET Subsequently fits inner skull, outer skull and outer scalp using T1- and T2- weighted images

15 Noise Reduction eg UCLA BrainSuite anisotropic smoothing Reduces noise» Important for subsequent steps» Often need to minimise smoothing of boundaries and important features Three iterations of the BrainVisa anisotropic diffusion filter

16 Segmentation / Classification One or multiple images as input (T1, PD, T2) Images skull stripped If more than one input image then need to be co-registered

17 Segmentation / Classification Normal appearing anatomy» 3 peaks GM/WM/CSF Patient groups» 4 peaks potentially» Lesions / tumours / WMH appear as a 4 th peak

18 Standard Space Common reference coordinate system Register all members of a group to this space for group studies Original Talairach & Tournoux coords based on one postmortem brain Now use standard images based on non-linear group average (MNI152) MNI is not quite Talairach Text & images from FSL presentation

19 Other Spaces fmri Structural Standard Different images different spaces» e.g. standard space, structural space, functional space Can have different resolution images in the same space» e.g. 1mm and 2mm versions of standard space images Want to move image-related info between spaces» e.g. a mask from standard space to structural space

20 Other Spaces fmri Structural Standard Need to register spaces together (via images) and get the transformations before transforming/moving/resampling any image-related info (like masks) Can have versions of the same image (e.g. a mask) in several different spaces Image pipelines often move things between spaces

21 Cortical Thickness Measurement

22 Anatomical Labelling Volume or surface atlases» Use models of anatomy and image intensity

23 Display Plain images» BrainVisa Multiple cut-planes» BrainVisa Volume rendering» BrainVisa

24 To GUI or not to GUI? GUI Command line» brainextract imagein imageout» segment options skullstrippedin segmentedout Shell scripting» Powerful» Flexible» Worth the time to learn

25 Shell Scripting Scripting allows you to:» encapsulate common lists of commands in a file» automate/batch processes» make new flexible and configurable tools» understand other people's scripts/tools

26 Shell Scripting Common scripting languages include:» Shell scripts - sh, bash, csh, tcsh» Other scripting languages - TCL, Perl, Python Example #!/bin/sh for filename in *.nii.gz ; do fname=`$fsldir/bin/remove_ext ${filename}` fslmaths ${fname} -s 2 ${fname}_smooth2 mv ${fname}.nii.gz ${fname}_smooth0.nii.gz done

27 FSL (Oxford) BET brain extraction tool FAST tissue-type segmentation FIRST sub-cortical structure segmentation SIENA atrophy analysis FSL-VBM voxelwise grey-matter density analysis

28 FSL (Oxford) SIENA» Serial analysis of smri

29 BrainSuite (UCLA) Skull stripping Tissue classification Surface generation Several other tasks Requirements» Microsoft Windows» 3 GHz Pentium 4, 1Gb RAM, OpenGL graphics card

30 BrainSuite (UCLA)

31 CIVET (MNI) N3 uniformity correction» A standard package used in many other pipelines INSECT gm/wm/csf segmentation» Artificial neural network approach ANIMAL» Regional volume segmentation tool CLASP cortical thickness extraction» Very accurate, but no cortical parcellation schema Documentation limited

32 CIVET (MNI)

33 Freesurfer (Fischl & Dale)

34 Freesurfer (Fischl and Dale) Command line drive Linux package Particularly good T1 volume pipeline Requires very good quality data» 2 T1 volumes recommended Manual review and editing of several pipeline stages required» Up to 30% of data requires manual editing

35 BrainVisa (France) Powerful suite of programs Many additional packages available Very good visualisation tools Requires some degree of IT competence to make the most of advanced features

36 BrainVisa (France) Identification of gyri a particular strength

37 CIVET (MNI)

38 CIVET (MNI)

39 Quality Control of Pipeline Results Most pipelines require good images to work optimally» Definition of good does vary» eg Good SNR, minimal subject movement, no metallic artefacts etc Good pipelines allow easy review of results» Eg Generation of contact sheets of results Always visually assess the results of pipelines

40 Quality Control of Pipeline Results Use visual overlay of results, or use display tool to flick between results and original image Good Poor

41 Image Databasing & Pipelines

42 Image Databasing Can load pipeline results into compatible databases

43 How to Choose Between Pipelines? Hardware / software availability / preference» Mac / PC / Cluster, Windows XP / Linux / Unix Amount of coding you are able / willing to do» Learning shell scripts likely to save lots of time Number of datasets» Time you have Quality of results

44 Quality of Results Short term reproducibility Medium term reproducibility Long term reproducibility Accuracy Healthy controls or abnormal patient groups? Tolerance of image artefacts

45 Clinical Use? Clinical use

46 Conclusions Image pipelines are» Powerful research tools» Changing and improving every year Watch out for frequent version changes» Variable in terms of quality The best tool for your task may not be part of your faviurite pipeline» Not a panacea» But still very good!

47 Links