1 / 20

Region of Interest Queries in CT Scans Matthias Schubert 1 Joint work with

Region of Interest Queries in CT Scans Matthias Schubert 1 Joint work with Alexander Cavallaro 2 , Franz Graf 1 , Hans-Peter Kriegel 1 , Marisa Thoma 1 1 Ludwig- Maximilians - Universität München , Database Group 2 Imaging Science Institute, University Hospital Erlangen. Outline.

slade
Download Presentation

Region of Interest Queries in CT Scans Matthias Schubert 1 Joint work with

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Region of Interest Queries in CT Scans Matthias Schubert1 Joint work with Alexander Cavallaro2, Franz Graf1, Hans-Peter Kriegel1, Marisa Thoma1 1 Ludwig-Maximilians-UniversitätMünchen, Database Group 2 Imaging Science Institute, University Hospital Erlangen

  2. Outline • ROI Queries on CT-Scans • ROI Retrieval Based On a General Height Scale: • Simple Solution based on Similarity Search • Solution based on Generalized Height Scale • kNN Regression for mapping slices • Iterative interpolation • Experimental Validation • Summary

  3. Computer Tomography:CT Scans Head => • 3-dimensional grid of 12 bit grey values • Depending on resolution: few MB to multiple GB (Example: 2.25 MB) • Image strongly depends on the used scanner and the scan parameters • DICOM header contains some meta information for each slice • DICOM headers are mostly empty or even misleading y Height axis z x <= Feet 23000CWZ8S.0001145710.4.1 1, 16, 31, 46, 61, 76, 91, 106, 121

  4. Picture Archievingand Communication Systems • Scans are stored in Picture Archiving and Communication Systems(PACS) • Scan retrieval by patient name, time, DICOM information • Querying parts of scans is not supported very well=> load and transmit complete scan • No access to sub volumes specified by an example

  5. Problems • slices outside the ROI increase the transfer volume • bottleneck is the LAN: • large transfer times (up to minutes) • bandwidth in LAN is a limiting factor • Only transferring the ROI requires tracking it • slice numbers in the target scan are not the same as in the query scan (scan regions vary) • positions might vary between scans and patients • (organ positions vary)

  6. Region of interest Query CT scan vq User-defined 3D ROI Examplescanvq + chosen ROI Locate ROI Client ID of CT scan vi Target scanvi on remote Server Image database(PACS) Server Trans-fer ROI target scan vi in PACS Client Matching ROI in targetscanvi result

  7. Outline • ROI Queries on CT-Scans • ROI Retrieval Based On a General Height Scale: • Simple Solution based on Similarity Search • Solution based on Generalized Height Scale • kNN Regression for mapping slices • Iterative interpolation • Experimental Validation • Summary

  8. Localization via Similarity Search • Short Commings: • Requires pre-processing or heavy load on server:For each slice in target scan: • Feature Transformation • Comparison to ROI • Feature similarityisinfluencedby global scansimilarity: • Scan parameters • Patient characteristics • => Directsimilarityoftenfails Examplescanvq + chosen ROI Locate ROI Target scanvi on remote Server Trans-fer ROI Matching ROI in targetscanvi

  9. ROI Query Based on a Generalized Height Scale ID of CT scan vi Examplescanvq + chosen ROI Locate ROI CT scan vq User-defined 3D ROI Client H Instance-based Regression Target scanvi on remote Server H gen. height scale H Server Iterative Interpolation Trans-fer ROI CT scan vi in PACS Client Matching ROI in targetscanvi result Height axis (Hscan)

  10. Instance-based Regression: scan→ H Emrich et al: CT Slice Localization via Instance-Based Regression, SPIE‘10 Examplescanvq + chosen ROI Locate ROI H k-NN query: Training Database: 2D imagefeaturesofheight-annotated CT slicesofmultiplescans H Consensus heighth  H H Target scanvi on remote Server • Speed-up Measures: • Dimension reduction: RCA • Spatial Indexing: X-Tree • Better: Large training set • Provides multiple examples annotated within consensus height space H • More stable results Bar-Hillel et al: Learning distancefunctions usingequivalencerelations, ICML‘03 Berchtold et al: The X-Tree: An index structure for highdimensional data, VLDB‘96

  11. Iterative InterpolationH→ scan • Combine Regression Mapping with Interpolation vi height space H 3 3 1 1 2 2 CT scan vi (in PACS) 0 H 1 2 3 Estimate location of vi in H via regression Interpolate target positions and . for hlb and hub Verify target positions via regression Accept Result Refinement Interpolation

  12. Outline • ROI Queries on CT-Scans • ROI Retrieval Based On a General Height Scale: • Simple Solution based on Similarity Search • Solution based on Generalized Height Scale • kNN Regression for mapping slices • Iterative interpolation • Experimental Validation • Summary

  13. Quality of Height Regression (scan→ H) Quality andRuntimew.r.t. Training Database size Main Memory Runtimes on original, 175-dimensional Image Features On-disc runtime for dataset of 2103 CT scans (= 0.9 Mio slices) after RCA dimension reduction + X-Tree Indexing: dim 10 => 20 ms With feature generation and dimension reduction: Time / Query = 40 ms Error = 1.98 cm

  14. Validation of ROI Query Pipeline • Testing Height Range Queries on 5 manually-annotated Landmarks in 33 CT Scans: • Annotation Error (LB + UB): 2.6 cm • ROI Query Error (LB + UB): 2.6 – 2.4 cm • ROI Query Runtimes: 1.3 – 10 seconds Pays off if 8 slices are saved sacralpromontory lowerxiphoidprocess lowerplateofthe 12ththoracicvertebra cranialsternum lowerboundofcoccyx Volume oforigin: 23000BVEFR.0000740833.11.1

  15. Runtime Advantages 20 runtime 20 % retrievedslices 15 15 % Runtime [sec] 10 Retrievedfractionofcompletevolumes 10 % 50 5 % 0 0 % Left kidney Urinary bladder Hip to lower L5 Arch of aorta 16.8 cm 9.6 cm 4.7 cm 0.9 cm • Retrieval Times forTypicalQueries: Test on 20 CT scans of 12,000 slices Complete Retrieval time: 70 s per scan => 70 to 99 % reduction of the retrieved volumes

  16. Outline • ROI Queries on CT-Scans • ROI Retrieval Based On a General Height Scale: • Simple Solution based on Similarity Search • Solution based on Generalized Height Scale • kNN Regression for mapping slices • Iterative interpolation • Experimental Validation • Summary

  17. Conclusionand Outlook • Introduced ROI Query Framework: • Great speed-upof CT subvolumeretrievalqueries • Low costsandlowerroroflocalization • Example-basedqueriesare extensible toqueriesusinganatomicalatlases • Future Work: • Extension ofheightqueriestoarbitrary 3D queries • Test on alternative, non-medicalusecases

  18. Thank you.

  19. Backup: Quality of Height Regression (scan→ H) Increased Quality andRuntimewith Database size Main Memory Runtimes RCA dimension reduction + X-Tree Runtimes

  20. Backup: Runtime Advantages • Simulated real-wolrdqueriesofvaryingheights: For 20 CT scans of 12,000 slices: total retrieval time = 1,400 seconds => 70 to 99 % reduction of the retrieved volumes

More Related