liverbench nbt oct_2013
TRANSCRIPT
Liver WorkbenchLiver Workbench −
An Integrated Tool-Suite for Liver Components Segmentation, Quantification and Surgical Planning from CT Data
ZHOU Jiayin*, CHEN Wenyu, HUANG Weimin, XIONG Wei, Thiha OO
Institute for Infocomm Research (I2R), A*STAR, Singapore
A project funded by the Joint Council Office (JCO), A*STARA project funded by the Joint Council Office (JCO), A*STAR
LIU Jimin, CHI Yanling, TIAN QiSingapore Bio-imaging Consortium (SBIC), A*STAR, Singapore
Sudhakar K. VENKATESH
National University Hospital (NUH), Singapore
Contact: 6408-2497, [email protected]: 6408-2497, [email protected] WorkbenchLiver Workbench11/2012Liver WorkbenchLiver Workbench11/2012
Motivation
Liver cancer: serious threaten to human health with 0.6-1.0 M new cases per year
Surgical resection / transplantation offers the best prognosis
Precise liver surgery expands the availability of liver surgery
Surgery planning has increasing demands for quantitative analysis of liver components
Gross liver, liver segments, tumors, vascular structure……
Objective
Construct a liver CT image database with associated ground truth for benchmarking and building statistical models
Develop a Liver WorkbenchLiver Workbench with 3D liver object segmentation, modeling and quantification toolkits
Clinical applications: tumor volumetry, tumor characterization and surgical planning
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Liver Workbench (An image-based liver workbench with 3D liver object segmentation, modeling and quantification toolkits for clinical applications)
Oct 2009 ~ Apr 2013, a JCO funded project collaborating with SBIC and NUHS
Project Architecture
Surgical resection / transplantation offers the best prognosis for liver cancer treatment. Surgery planning has increasing demands for quantitative analysis of liver structures. A Liver Workbench with 3D liver object segmentation, modeling and quantification
toolkits is being developed to explore various of clinical applications.
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Liver 3D object segmentation(Liver, tumor, vessel, etc)
Liver 3D object segmentation(Liver, tumor, vessel, etc)
Liver 3D object quantification,validation & modeling
Liver 3D object quantification,validation & modeling
Liver 3D model interaction &visualization
Liver 3D model interaction &visualization
Clinical applicationsClinical applications
ProbabilisticAtlas
ProbabilisticAtlas
CT/MRIDatabaseCT/MRI
Database
3D liver/tumorvolumetry
3D liver/tumorvolumetry
Tumor typecharacterization
Tumor typecharacterization
Pre-operativeplanning
Pre-operativeplanning More….More….
3D Liver & Liver Tumor Segmentation 3D Liver Vasculature Extraction Modeling: Construction of Probabilistic Liver Atlas
Focal Liver Lesion Detection & Characterization
Surgical Planning for Transplant and Tumor Removal
Modules / Technologies Developed
1. A robust platform to segment and quantify liver and its component from CT scans;
2. A CADx system to detect and characterize focal liver lesions;
3. An intuitive and flexible way to plan liver surgery interactively;
4. Support clinical decision-making and biomedical research.
Important Features
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3D Liver Segmentation (WACV 09’, RSNA 09’)
• 3D Liver Volume Segmentation by Flipping-free Mesh Deformation and Registration
Uses explicit quadrilateral mesh representation and Laplacian deformation for the purpose of efficiency;
Solves self-intersection problem by detecting and discarding possible flippings on mesh surface before each iteration;
Incorporates shape constraints to reduce sensitivity to noise;
Easy to implement
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Test on clinical CT volume - liver segmentation 20 sets of CT-scan data, with slice thickness from 1-3 mm Compared with level-set and 2D grab-cut.
The dynamic evolution procedure
3D Liver Segmentation
Min. Max. Mean STD. Median
Relative average volume difference (RAVD, %) 0.0 30.8 7.1 8.7 3.5
Volumetric overlap error (VOE, %) 6.6 36.3 12.3 7.1 9.9
Average symmetric surface distance (ASSD, mm) 1.1 10.5 2.5 2.1 1.8
With flip avoidance
W/o flip avoidance
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Liver Tumor Segmentation (MICCAI-MLMI 11’, EMBC 13’)
• Liver Tumor Segmentation by Hybrid Support Vector Machine (SVM) Classifier
Combination of the advantages of one class SVM and binary SVM
Automatic generation of balanced training data
Results from one single study
Working steps
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Liver Tumor Segmentation
Overall segmentation of the liver, liver tumor and gallbladder
Test on clinical CT volume - liver tumor segmentation 15 sets of CT-scan data with 26 tumors, with slice thickness from 1-3 mm 13 for parameters tuning and 13 for test
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Liver Vessel Segmentation (IEEE-TBME 11’)
• Liver Vessel Segmentation by Vessel Context-based Voting The liver has an unique dual blood supply
system – Hepatic artery, portal vein and hepatic vein
Hepatic vascular structure determines the partitioning of liver segments
Surgical planning requires accurate analysis of vascular structure
Working steps
Grouping (branch-level)
Vessel Context Extraction/Update
CT volume & liver mask
vasculature
branch
vessel context
Post-processing
Grouping (voxel-level)
(a)
Touching Vessels
Seperated Vessels
By level set Proposed
Over-segmented Under-segmentedLiver WorkbenchLiver Workbench11/2012Liver WorkbenchLiver Workbench11/2012
• Construction of A Probabilistic Liver Atlas
Liver Structure Modeling (ICIP 09’, RSNA 09’)
An pair of atlases encoding probabilities of liver anatomic and structure variabilities An atlas retaining densitometric mean An atlas retaining spatial variance
Helps segmentation, interpretation, group comparison, etc
Key task: To register images from different subjects to a common coordinate system
Registration based on dense correspondence of all voxels without landmarks
Multiple dataset registration is unbiased to all datasets registered
Registration is in infinite dimensional diffeomorphic space
Probabilistic analysis in both density and geometry
Tested using 30 CT scans, 5 mm section thickness
The proposed landmark-free registration method: The proposed landmark-free registration method:
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Unbiased registered multi-organs
The mean images (gray) and respective probabilistic atlases (red)
1 iteration
5 iterations
10 iterations
Anterior view Unbiased registered liverPosterior view
Liver Structure Modeling
1 2 3 4 5 6 7 8 9 100
2000
4000
6000
8000
10000
12000
14000
Iterations
MSE
MSE5
MSE10
MSE15MSE20
MSE25
Registration convergence of
mean square errors
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Arterial Portal vein Delayed
Liver Lesion Detection & Characterization (SPIE 11’, RSNA 11’)
Visual detection of small-size focal liver lesions (FLLs) can be difficult;
Characterizing FLLs is usually experience-dependent;
Detect focal liver lesion by subtracting normal liver parenchyma and vessels from liver region.
Characterize focal liver lesion using similarity retrieval based on multiple phase CT image features
Creation of database using 87 confirmed cases with 6 types Leave-one-out for testing using multiple parameters
Texture feature and its derivatives Density feature and its derivatives
Easy retrieval of lesions with different pathology but similar appearances Retrieval of lesions with same pathology but different appearances Assist in decision-making on radiological diagnosis by providing evidence Train medical students and radiological residents
Patent filedPatent filed
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Liver Lesion Detection & Characterization (IJCARS 13’, Med Phys 13’)
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IJCARS 13’IJCARS 13’
Medical Physics 13’Medical Physics 13’
ART
NC
PV
DL
Load Query
Preprocessing
FLL detection
FLL retrieval
Reporting
Top 1 Top 2 Top 3 Top 4 Top 5 Top 6 Top 7 Top 8
Top 9 Top 10 Top 11 Top 12 Top 13 Top 14 Top 15 Top 16
#1
#2
Query Similar cases 3D View
Interface
#1#2
Two big tumors are detected. Top 1 candidate: 104 ml and 83% similar to a confirmed FNH.
Top 2 candidate: 155 ml and 88% similar to a confirmed cyst.
Retrieval results
Liver Surgery Planning (RSNA 12’, EMBC 13’, MICCAI-MIAR 13’)
• An Interactive Liver Surgery Planning System Comprehensive real-time 3D visualization and mesh deformation
Plan, design and adjust the resection map with graft/remnant volumetry
Automatic guarantee of the safety margin with the minimal resection surface
The Main User Interface
Volumes of lobes and the percentages
Planning of hemi-hepatectomy with MHV preservationLiver WorkbenchLiver Workbench11/2012Liver WorkbenchLiver Workbench11/2012
Adjust the Resection Surface to MHV Harvesting
Update the volume change
Liver Surgery Planning
Left and right lobes with PV
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Example: A live tumor in Segment III for resection
Liver Surgery Planning
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Liver, vasculature and tumor are segmented from CT data and the 3D graphical model is created.
Anterior-superior view
posterior-superior view
Liver Surgery Planning
Only show hepatic vein (HV)
Only show portal vein (PV)
Show 10 mm tumor margin (red sphere)
A rough hepatectomy resection plane, with the constraint to 10 mm tumor margin
A more precise resection surface, with the constraint to 10 mm tumor marginLiver WorkbenchLiver Workbench
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Liver Surgery Planning
Tumor safety margin, resected and remnant volumes
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Liver Surgery Planning
A more precise planning, the resected volume restricted within Segment III
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Liver Surgery Planning
Mapped with the original CT slices
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Liver Surgery Planning
1. A robust platform to segment and quantify liver and its component from CT scans;
2. A CADx system to detect and characterize focal liver lesions;
3. An intuitive and flexible way to plan liver surgery interactively;
4. Support clinical decision-making and biomedical research / drug development.
Liver surgical planningSegmentation of a liver with its components
Summaries
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Cum Laude Award
RSNA 12’