Automatic Vertebrae Localization in Spine CT using Decision Forests

Transcription

1 Automatic Vertebrae Localization in Spine CT using Decision Forests 1, Angel Alberich-Bayarri 1,2, Belén Fos-Guarinos 1, Fabio García-Castro 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 Outline Introduction Purpose Materials and methods Decision Forests Dataset Detection based on Regression Forests Refinement based on the spinal canal detection Results Conclusions

3 Introduction In clinical routine practice, when dealing with spinal abnormalities and pathologies, the localization and identification of vertebral bodies is a crucial step for an appropriate clinical diagnosis, surgical planning and follow-up assessments.

4 Purpose Nowadays, vertebrae identification is a manual task that hinders radiologists workflow Create pipelines to locate and identify automatically vertebrae in CT scans to help radiologists to perform diagnosis in a shorter period of time

5 DECISION TREE Supervised learning: annotated training data Input data: intensity-based features from different voxels of the image Internal (split) node Root node Output data: distance from the voxels to each vertebra Terminal (leaf) node

6 DECISION FOREST Tree parameters optimized over a randomly sampled subset of all possible features to minimize high fitting bias Ensemble of different trees to reduce overfitting The forest output is the average of all tree outputs

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9 DATASET Dataset preparation for training the forest algorithm 232 spine CT scans o o 80% training 20% testing Arbitrary field of view Healthy and pathological cases

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

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12 FEATURE EXTRACTION 30,000 randomly-selected training voxels from the filed-of-view of the image. 40 x 40 x 120 mm 3 patch around each voxel. Each patch is divided into 10 x 10 x 30 mm 3 blocks. Mean intensity calculation from each box. 256 features associated to each training voxel.

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14 Random Regression Forest Training f 1,1 f 2,1 f 3,1.... f 256,1 d T1,1 d T2,1 d T3,1.... d S1,1 f 1,2 f 2,2 f 3,2.... f 256,2 d T1,2 d T2,2 d T3,2.... d S1,2 f 1,3 f 2,3 f 3,3.... f 256, d T1,3 d T2,3 d T3,3.... d S1, d i = φ(fi) REGRESSION FOREST TRAINING f 1,n f 2,n f 3,n.... f 256,n d T1,n d T2,n d T3,n.... d S1,n

15 Random Regression Forest Testing Unseen CT scan Feature Extraction RRF Testing Centroid Estimation c i = d i + X i

16 High variability in spine curvatures Refinement step to adapt the centroid detection to the patient-specific vertebrae position SPINAL CANNAL DETECTION

17 Refinement Based on the Spinal Canal Detection

18 Refinement Based on the Spinal Canal Detection Predicted vertebrae position after RRF Predicted vertebrae position after refinement Real position

19 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 L1 L2 L3 L4 L5 S1 T1 T2 T3 T4 T4 T6 T7 T8 T9 T10 T11 T12 L1 L2 L3 L4 L5 S1 Identification Rate (%) Median localization error (mm) Results Vertebra x y z

20 Results Region Median (mm) Mean (mm) STD (mm) Id. Rate All % Thoracic % Lumbar %

21 Conclusions We developed a method that provides the lowest error in the automatic detection and identification of vertebrae in CT scans. Vertebrae identification can be addressed on arbitrary field-ofview scans. This improves the radiological workflow in spine evaluation through computed tomography and allows the creation of automatic pipelines for the calculation of vertebrae bone microarchitecture characteristics.

22 Acknowledgements Ana Penadés Economic & Financial Manager Administrat ion Enrique Ruiz CTO Developme nt Alejandro Rodríguez PhD Image Analysis Engineer Amadeo Ten Image Analysis Engineer Imaging Biomarkers Analysis Sara Carratalá Neuroradiolo gy Sandra Pérez Data Manager Francisco Alcaide MRI Technician & PREBI Clinical Trials & PREBI Ángel Alberich- Bayarri, PhD. GIBI Scientific Technical Director & QUIBIM Founder and CEO Luis Martí Bonmatí MD, PhD. GIBI General Director & QUIBIM Founder GIBI230 & QUIBIM Directors Mª Carmen Rodríguez Team Coordinator Katherine Wilisch Marketing & Communicati ons Manager Encarna Sánchez Business Developer & Project Manager Rafael Hernández Navarro CTO Alejandro Mañas Full Stack Senior Developer Fabio García Castro R&D Responsible NEURO & MSK Alfredo Torregros a Image Analysis Scientist ONCO Belén Fos Guarinos Image Analysis Scientist LUNG Ana Jiménez Pastor Image Analysis Scientist LIVER Irene Mayorga Clinical Trials Coordinator Raúl Yébana Image Analysis Technician Management Development R+D and Imaging Biomarkers Analysis Clinical Trials

23 Automatic Vertebrae Localization in Spine CT using Decision Forests 1, Angel Alberich-Bayarri 1,2, Belén Fos-Guarinos 1, Fabio García-Castro 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