Automatic Vertebrae Localization in Pathological Spine CT using Decision Forests

Transcription

1 Automatic Vertebrae Localization in Pathological Spine CT using Decision Forests Ana Jiménez-Pastor 1, Esther Tomás-González 1, Ángel Alberich-Bayarri 1,2, Fabio García-Castro 1, David García-Juan 1, Luis Martí-Bonmatí 1,3 1 QUIBIM S.L., Valencia, Spain 2 La Fe Health Research Institute,Valencia, Spain 3 La Fe Radiology Department,Valencia, Spain

2 Introduction In clinical diagnosis, therapy and surgical intervention, the identification of vertebral bodies is a crucial step

3 Introduction Purpose Materials and methods Decision Forests Building input data Building output data Training Testing Results Conclusions Outline

4 Purpose MAIN PROBLEM: In spine pathologies it takes much time to radiologists to perform diagnosis because they have to label manually all vertebrae HELP RADIOLOGISTS TO PERFORM SPINE DIAGNOSIS IN A SHORTER PERIOD OF TIME USE AI METHODS TO LOCATE AND IDENTIFY AUTOMATICALLY VERTEBRAE IN SPINE CT SCANS CREATE PIPELINES FOR THE CHARACTERIZATION OF BONE STRUCTURES IN PATIENTS

5 DECISION FOREST Supervised learning: annotated training data Input data: set of Internal (split) node Root node 7 features (location & intensity-based features of each voxel) Output data: centroid probability maps Terminal (leaf) node

6 BUILDING TRAINING DATA CT SCANS INPUT DATA OUTPUT DATA

7 BUILDING TRAINING DATA CT SCANS INPUT DATA OUTPUT DATA

8 BUILDING TRAINING DATA. DATASET Dataset preparation for training the forest algorithm spine CT scans - Arbitrary field of view - Healthy and pathological cases

9 BUILDING TRAINING DATA CT SCANS INPUT DATA OUTPUT DATA

10 BUILDING TRAINING DATA CT SCANS INPUT DATA OUTPUT DATA Vertebral centroid location

11 - Position (x,y,z coordinates) - Vertebral body name

12 CONTEXTUAL INFORMATION Intensity-based features: Mean intensity over a 3D cuboid displaced with respect to the reference voxel position. Cuboid size and displacement are chosen randomly.

13 BUILDING TRAINING DATA CT SCANS INPUT DATA OUTPUT DATA

14 BUILDING TRAINING DATA CT SCANS INPUT DATA OUTPUT DATA Centroid probability maps

15 BUILDING OUTPUT DATA. PROBABILITY MATRICES Compute the probability of each voxel to be part of each vertebra ψ v x = e c v x 2 h v with x Ω I Centroid likelihood distribution for each vertebra ψ B x = 1 max v ψ v (xሻ Likelihood function for background label p l x = ψ l (xሻ σ mεl ψ m x with l L = V B Labeling distribution B. Glocker, D. Zikic, E. Konukoglu, D. R. Haynor, and A. Criminisi. Vertebrae localization in pathological spine ct via dense classification from sparse annotations. In Medical Image Computing and Computer-Assisted Intervention, pages Springer, 2013.

16 TRAINING X1 Y1 Z1 I1 F Fm1 X2 Y2 Z2 I2 F Fm2 X3 Y3 Z3 I3 F Fm Xn Yn Zn In f1n.... Fmn Pc11 Pc Ps11 Pc12 Pc Ps12 Pc13 Pc Ps Pc1n Pc2n.... Ps1n REGRESSION FOREST

17 TESTING X1 Y1 Z1 I1 F Fm1 X2 Y2 Z2 I2 F Fm2 X3 Y3 Z3 I3 F Fm Xn Yn Zn In f1n.... Fmn TRAINED REGRESSION FOREST Pc11 Pc Ps11 Pc12 Pc Ps12 Pc13 Pc Ps Pc1n Pc2n.... Ps1n

18 Results Expert annotation

19 Results Predicted location Expert annotation

20 Results VERTEBRAE AVARAGE DISTANCE (mm) STANDARD DEVIATION(mm) VERTEBRAE AVARAGE DISTANCE (mm) STANDARD DEVIATION(mm) T1 9,28 8,63 T2 9,31 6,87 T3 7,04 5,33 T4 3,02 3,52 T5 3,64 2,04 T6 4,96 2,82 L1 1,31 1,47 L2 4,92 4,55 L3 9,22 5,99 L4 15,29 10,15 L5 12,68 14,24 S1 11,01 10,57 T7 4,93 1,94 T8 5,83 4,46 T9 4,71 3,32 T10 3,01 0,45 T11 5,84 4,41 Average distance between the real and the predicted location less than 15 mm T12 3,88 2,68

21 Results REGION AVARAGE DISTANCE (mm) STANDARD DEVIATION (mm) THORAX (T1-T12) 5,45 2,15 LUMBAR (L1-L5) 10,88 5,16 Better classification in thorax region due to the variability inherent to the lumbar region shape

22 Conclusions We created pipelines for the automatic localization and identification of vertebrae in CT scans The detection method will allow radiologists to perform the spine diagnosis in a shorter period of time. Automatic localization and identification of vertebral bodies can be addressed by AI methods to create pipelines for the characterization of bone structure in patients.

23 Acknowledgements Luis Martí Bonmatí MD, PhD.GIBI PI andquibim Founder ÁngelAlberich-Bayarri PhD.GIBI DirectorandQUIBIMCEO QUIBIM Staff Fabio GarcíaCastro- M.Sc Rafa Hernández Navarro - B.Sc DavidGarcía - M.Sc Encarna Sánchez - M.Sc KatherineWilisch Ramírez - M.Sc Irene Mayorga Ruiz - M.Sc Belén FosGuarinos -InternshipStudent Ana Jiménez Pastor -InternshipStudent Chief Scientific Officer Chief Technology Officer Back-End Development of Imaging Biomarkers Chief Marketing Officer GIBI2 30 Staff Enrique Ruiz Martínez M.Sc AmadeoTen Esteve M.Sc Ana Penadés-Adm. AlfredoTorregrosa-InternshipStudent Carlos Moya-InternshipStudent MS Biomedical Engineering Coordinator and CEO Support Imaging Study Coordinator Image Analysis Scientist Artificial Intelligence Artificial Intelligence Chief Financial Officer Imaging Biomarker Developer Imaging Biomarker Developer