=Paper=
{{Paper
|id=Vol-1194/visceralISBI14-2
|storemode=property
|title=Automatic Liver Segmentation Using Multiple Prior Knowledge Models and Free-Form Deformation
|pdfUrl=https://ceur-ws.org/Vol-1194/visceralISBI14-2.pdf
|volume=Vol-1194
}}
==Automatic Liver Segmentation Using Multiple Prior Knowledge Models and Free-Form Deformation==
https://ceur-ws.org/Vol-1194/visceralISBI14-2.pdf
Automatic Liver Segmentation using Multiple Prior
Knowledge Models and Free-Form Deformation
Cheng Huang, Xuhui Li, Fucang Jia
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
1068 Xueyuan Avenue, Xili University Town, Shenzhen, 518055, China
Email: fc.jia@siat.ac.cn
Abstract
In this paper, an automatic and robust coarse-to-fine liver im-
age segmentation method is proposed. Multiple prior knowl-
edge models are built to implement liver localization and seg-
mentation: voxel-based AdaBoost classifier is trained to lo-
calize liver position robustly, shape and appearance models
are constructed to fit liver shape and appearance models to
original CT images. Free-form deformation is incorporated
into segmentation process to improve the model’s ability of
refining liver boundary. The method was tested on IBSI 2014
VISCERAL challenge datasets and the result demonstrates
that the proposed method is robust and efficient.
1 Introduction
Accurate and robust liver segmentation in CT images is an indispensable part in liver quantitative
diagnosis and surgery planning, while variation in liver shape, appearance and fuzzy boundary
remain challenging. Recently, prior knowledge models learned from big data play an important
role in successful clinical image segmentation. In this study, integrating of discriminative and
generative models in a hybrid scheme was presented to assist liver localization and segmentation:
machine learning based voxel classifier, active shape model (ASM) [Cootes95] including statistical
shape model (SSM) prior and local appearance model. Finally, the final fitted model was free-
form deformed to true liver boundary under appearance model guidance. The coarse-to-fine liver
image segmentation framework including liver localization, model reconstruction, model fitting and
free-form deformation is illustrated in Figure 1.
Copyright c by the paper’s authors. Copying permitted only for private and academic purposes.
In: O. Goksel (ed.): Proceedings of the VISCERAL Organ Segmentation and Landmark Detection Benchmark at
the 2014 IEEE International Symposium on Biomedical Imaging (ISBI), Beijing, China, May 1st , 2014
published at http://ceur-ws.org
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Huang et al: Liver Segmentation using Model-based Free-Form Deformation
Figure 1: The four steps of liver segmentation framework: (a) liver model location; (b) registration
with liver distance map; (c) shape fitting under appearance guidance; (d) free-form deformation.
2 Method
2.1 Liver localization
An atlas image based rigid registration with correlation coefficient histogram metric was used to
detect the region of interest (ROI) of liver. A set of image features such as region mean intensity,
variance, location, histogram and contextual features were extracted to train an AdaBoost classifier,
by which a liver probability map was generated, and the position of the liver was robustly estimated.
2.2 Model reconstruction
The SSM of liver was constructed from training CT images and corresponding binary segmenta-
tions. Firstly, pose training described in [Huang13] was applied to resample all the images. For
shape correspondence establishment, one reference mesh was obtained by marching cubes method,
all other training segmentations were elastic registered to the reference mesh, landmarks were sam-
pled equally on each training mesh. The SSM was constructed by Statismo toolkit [Luthi12] and
represented by simplex mesh.
The local appearance model of liver was established by a K Nearest Neighbor (KNN)-classifier
trained on both intensity and gradient profiles information inside, outside and at the true liver
boundary as suggested in [Heimann07]. For each landmark, profiles perpendicular to the surface
are sampled from all training volumes and stored as boundary samples. Additional non-boundary
samples were acquired by shifting the profiles towards the inside and outside of the liver.
2.3 Shape and appearance profile fitting
For the image to be segmented, a liver probability map was derived by AdaBoost classifier, and
the binary mask can be obtained at threshold 0.5. The distance map image was applied to register
to the point sets of the mean shape model, and the mesh vertexes of deformed mean shape were
fitted to liver boundary location with major shape variation constraints.
The appearance model is utilized to drive the model toward the precise liver boundary. Local
appearance features for all landmarks are extracted at different positions perpendicular to the model
surface. Previous trained KNN-classifier shifts landmarks to the optimal displacement position with
maximum boundary probability.
2.4 Free-form deformation
Once appearance profile fitting has converged, the deformed shape model were then free-form
deformed to the more accurate position. Free deformation was implemented based on deformable
simplex mesh [Montagnat97] segmentation. The internal force strives to keep the deformable mesh
close to the best fitting SSM, and the external forces tries to move all vertices to the locations
where intensity or gradient appearance model predicts the highest boundary probability. Previous
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Huang et al: Liver Segmentation using Model-based Free-Form Deformation
KNN-classifier was integrated as external force to deform to conquer local specific variation of liver
shape.
3 Result
Seven CT and seven CTce IBSI VISCERAL challenge 2014 datasets were employed to train Ad-
aBoost classifier. Additional fifty manually segmented datasets were used to train the prior shape
and appearance models. There are 1252 landmarks in the liver shape model, each landmark is
sampled with 11 points in the landmark normal direction in the profile model. The experiment was
tested on 8 CT and 8 CTce datasets. The four evaluation metric scores are as follows: average dice
coefficient were 0.924 and 0.925, interclass correlation were 0.924 and 0.925, adjusted rand index
were 0.923 and 0.920 and average distance were 0.222mm and 0.261mm for CT and CTce modality
respectively.
4 Conclusion
In this paper, a robust and automatic liver segmentation method is proposed. The method exploits
different prior knowledge to represent contextual, profile appearance and shape variation of liver,
relies on different registration to construct liver model, liver localization, model fitting and refined
deformation. The method has been validated on ISBI VISCERAL challenge and showed good
performance. In future, we will adapt the method to other visceral organs segmentation.
5 Acknowledgments
The work was supported by the National High-tech R&D Program of China (863 Program)
(No.2012AA022305).
References
[Cootes95] T. Cootes, C. J. Taylo, D. H. Cooper, J. Graham. Active shape models - their training
and application. Computer Vision and Image Understanding, 61(1):38–59, 1995.
[Huang13] C. Huang, F. Jia, C. Fang, Y. Fan, Q. Hu. Automatic liver detection and segmentation
from 3D CT images: a hybrid method using statistical pose model and probabilistic
atlas. International Journal of Computer Assisted Radiology and Surgery, 8(S1):237-
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[Heimann07] T. Heimann, H. P. Meinzer, I. Wolf. A statistical deformable model for the seg-
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[Luthi12] M. Luthi, R. Blanc, T. ALbrecht, T. Gass, O. Goksel, P. Buchler, M. Kistler,
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[Montagnat97] J. Montagnat, H. Delingette. Volumetric medical images segmentation using shape
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22, 1997.
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