liverbench nbt oct_2013

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

Liver WorkbenchLiver Workbench11/2012Liver WorkbenchLiver Workbench11/2012

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.


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


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


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


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


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


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:


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


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


Liver Lesion Detection & Characterization (IJCARS 13’, Med Phys 13’)


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


Example: A live tumor in Segment III for resection



Liver, vasculature and tumor are segmented from CT data and the 3D graphical model is created.

Anterior-superior view

posterior-superior view


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

11/2012Liver WorkbenchLiver Workbench11/2012


Tumor safety margin, resected and remnant volumes



A more precise planning, the resected volume restricted within Segment III



Mapped with the original CT slices



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


Cum Laude Award

RSNA 12’

liverbench nbt oct_2013

Education

liver components segmentation

singapore liver workbench

gallbladder liver workbench

d liver object segmentation

d object segmentation

d liver volume segmentation

d liver segmentation

liver tumor segmentation