Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5829_Библиотеки_им_академика_М_И_Перельмана.pdf
Real-Time 4D Cardiac Segmentation by Active Geometric Functions 249
and epicardial segmented surfaces, in terms of area difference, true positive volume
fraction, and false positive volume fraction. For the endocardial surface, the average
surface distance (mean ± standard deviation) was 8.7 ±5.9%, the true positive
volume fraction was 93.3 ± 7.0%, and the false positive volume fraction was
7.0±4.6%. For the epicardial surface, the averagesurface distance was 6.8±5.3%,
the true positive volume fraction was 95.5 ±3.6%, and the false positive volume
fraction was 7.8 ±5.2%. Average distance between automated segmented surfaces
and manually tracedsurfaces was 3.0±2.4 pixels. Comparison metrics froma recent
systematic study on cardiac MRI segmentation [31] were used as a reference, which
suggested that our results were comparable to level-set based methods as well
as inter-observer variability. Note that the PTI MRI images had slightly coarser
resolution as well as a slightly blurrier appearance than regular cardiac cine MRI
due to undersampling in the phase-encoding direction, which may increase interobserver variability as reported in [31].
6 Segmentation of Cardiac MR Perfusion Images
In this section, we illustrate the feasibility of AGF in multi-phase vector image segmentation on an application for dynamic cardiac MR perfusion image segmentation.
In order to segment the myocardial surfaces from cardiac perfusion time series, we
used the extension of AGF segmentation framework to vector-valued image space,
by treating the time course of each pixel intensity as a vector.
A TurboFLASH pulse sequence was employed on a whole-body 3T scanner
(Siemensc TIM Trio) equipped with a 12-elementcoil array. The relevant imaging
parameters included: FOV = 320 × 320 mm, image matrix = 128 ×128, slice
thickness = 8mm, flip angle = 10
pixel, saturation recovery time delay (TD)=10 ms, and repetition time = 40ms.
The AGF model was initialized in 2D as two small circles inside the endocardium
on the frame corresponding to peak blood enhancement. Manual tracing of the
endocardial and epicardial surfaces was also performed by an experienced expert
serving as a gold standard to evaluate the performance of the proposed multi-phase
multi-channel AGF segmentation framework. The algorithm was implemented in
Matlabc (Natick, MA).
It took the AGF segmentation framework 16 iterations to reach a stable endocardial and epicardial segmentation under a Matlabc implementation. The total
processing time was 31 ms. All computations were executed on a 2.3 GHz Intel
Xeon workstation, running Windows XP. Quantitative evaluations were performed
both on endocardial and epicardial segmented surfaces, in terms of area difference,
true positive volumefraction, and false positive volume fraction. For theendocardial
surface, the average surface distance was 6.8%, the true positive volume fraction
was 91.4%, and the false positive volume fraction was 1.9%. For the epicardial
surface, the average surface distance was 1.5%, the true positive volume fraction
was 97.8%, and the false positive volume fraction was 3.7%. These results are
◦
, TE/TR = 1.3/2.5ms, BW = 1,000Hz per
250 Q. Duan et al.
Fig. 14 Multi-phase and multi-channel AGF segmentation on cardiac perfusion MR images.
Signal intensity (SI) curves from manual and the AGF segmentation inside the LV blood pool
(left) and inside the myocardium (right)
comparable to a recent systematic study on cardiac MRI segmentation. Temporal
curves of the average signal intensity (SI) inside the left ventricular (LV) blood
pool and inside the myocardium were derived for both the AGF segmentation and
the manual tracing, as illustrated in Fig. 14. Average errors in the SI curves (mean
± standard deviation) were 1.8 ±0.7% for the LV blood pool and 2.8 ±0.7% for
the myocardium. The proposed AGF segmentation framework only required 31 ms
to segment the myocardium on the multi-phase MRI dataset. Besides offline data
analysis, the proposed method could be applied to other cardiac applications where
real-time feedback is preferred, as in MR guided interventions, or online processing
when combined to image acquisition and reconstruction.
7 Conclusion
The active geometric functions (AGFs) framework was presented as a new surface
representation framework for image segmentation with deformable models. Unlike
existing frameworks including level-set and parametric deformable models, the
AGF framework uses an (n−1) −D surface representation for an n-D object, based
on a close representation of the deforming shape with a shape function defined in
a curvilinear coordinate system. The underlying numerical model of the geometric
functions and thereformulation of the energy functional in terms of surface integrals
bring tremendous benefits in terms of computational efficiency and reduction in
complexity for both surface deformation and energy minimization.
Similarly to the level-set framework, the AGF framework can be seamlessly
extended to multi-phase (i.e., multi-objects) and multi-channel (or multi-modal)
image segmentation, as well as incorporate image or shape prior information,
thanks to the straightforward determination of the inside and outside of the curves
being deformed. By using the concept of interpolating shape functions, the AGF
Real-Time 4D Cardiac Segmentation by Active Geometric Functions 251
framework can be used, when combined with finite element patches, to capture
complex shapes with controlled smoothness. By incorporating repositioning and
reorientation, the capture range of the AGF framework can be largely increased, and
made less dependent on the initialization than with level-sets. In addition, similar
to the “signed-distance function” concept in the level-set framework, geometric
function values also have physical meanings, which can be directly used in clinical
measurements or as a coupling feature in multi-phase segmentation. In summary,
the similarities with the level-set formulation provide the AGF framework with the
flexibility to virtually import all the new concepts or energy functionals derived
for level-set formulation into an AGF framework. The only key difference between
these two frameworks is that the level-set formulation, which uses higher order
distance-type of functions as surface representation, can allow topological changes,
which is not the case with the AGF framework.
The performance and feasibility of the AGF segmentation framework were
quantitatively validated and demonstrated on three cardiac applications with large
datasets. In an extensive set of synthetic and clinical experiments, presented in
this chapter, the AGF segmentation framework was able to achieve real-time
segmentation performance, requiring only fractions of a second to reach a stable
position, and therefore exhibiting segmentation capabilities faster than actual image
acquisition rates. The AGF segmentation framework was not only faster than alternative deformable models implementations but also opens the door to online and
real-time segmentation is combined with image acquisition, which would greatly
benefit existingand potentialclinical applications suchas interventional approaches.
Therefore, besides being an extremely fast segmentation method for offline image
processing, the AGF framework also opens the door to online segmentation, which
could fully unleash the power of 4D medical imaging.
References
1. Herz S, Ingrassia C, Homma S, Costa K, Holmes J (2005) Parameterization of left ventricular
wall motion for detection of regional ischemia. Ann Biomed Eng 33(7):912–919
2. Udupa JK, Wei L, Samarasekera S, Miki Y, van Buchem MA, Grossman RI (1997) Multiple
sclerosis lesion quantification using fuzzy connectedness principles. IEEE Trans Med Imaging
16:598–609
3. Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. Int J Comput Vis
1:321–331
4. Sethian J (1999) Level set methods and fast marching methods vol 3. In: Cambridge
monographs on applied and computational mathematics, 2 edn. Cambridge University Press,
Cambridge
5. Osher S, Fedkiw R (2003) Level set methods and dynamic implicit surfaces In: Applied
mathematical sciences, vol 153. Springer, New York
6. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10
(2):266–277
7. Angelini E, Homma S, Pearson G, Holmes J, Laine A (2005) Segmentation of real-time threedimensional ultrasound for quantification of ventricular function: a clinical study on right and
left ventricles. Ultrasound Med Biol 31(9):1143–1158
252 Q. Duan et al.
8. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and
application. Comput Vis Image Underst 61(1):38–59
9. Cootes TF, Edwards GJ, Taylor CJ (1998) Active appearance models. In: Lecture notes in
computer science, vol 1407. Springer Berlin, pp 484–498
10. Held K, Kops ER, Krause BJ, Wells WM, Kikinis R, Muller-Gartner H-W (1997) Markov
random field segmentation of brain MR images. IEEE Trans Med Imaging 16(6):878–886
11. Boykov YY, Jolly M-P (2001) Interactive graph cuts for optimal boundary & region segmentation of objects in N–D images. In: Eighth international conference on computer vision
(ICCV’01) 2001, pp 105–112
12. Jin Y, Imielinska C, Laine A, Udupa J, Shen W, Heymsfield S Segmentation and evaluation
of adipose tissue from whole body MRI scans. In: Proceedings of the sixth international
conference on medical image computing and computer assisted interventions (MICCAI 2003),
Montreal, Nov 2003 pp 635–642
13. Mikic I, Krucinski S, Thomas JD (1998) Segmentation and tracking in echocardiographic
sequences: active contours guided by optical flow estimates. IEEE Trans Med Imaging 17
(2):274–284
14. Xu C, Prince JL (1998) Snakes, shapes and gradient vector flow. IEEE Trans Image Process 7
(3):359–369
15. Sethian JA (1999) Level set methods and fast marching methods: evolving interfaces
in computational geometry, fluid mechanics, computer vision, and materials science. In:
Cambridge monographs on applied and computational mathematics Cambridge University
Press, Cambridge
16. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10
(2):266–277
17. Mumford D, Shah J (1985) Boundary detection by minimizing functional. In: International
conference on computer vision and pattern recognition, San Francisco, pp 22–26
18. Angelini E, Laine A, Takuma S, Holmes J, Homma S (2001) LV volume quantification via
spatio-temporal analysis of real-time 3D echocardiography. IEEE Trans Med Imaging 20
(6):457–469
19. Song T, Angelini ED, Mensh BD, Laine A Comparison study of clinical 3D MRI brain
segmentation evaluation. In: Annual international conference IEEE engineering in medicine
and biology society (EMBS), San Francisco, 1–5 Sept 2004 pp 1671–1674
20. Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. In: Proceedings of
1st international conferences on computer vision pp 259–268
21. Duan Q, Shechter G, Gutierrez LF, Stanton D, Zagorchev L, Laine AF, Daniel Elgort (2007)
Augmenting CT cardiac roadmaps with segmented streaming ultrasound. San Diego, CA.
p 65090V65091-65011
22. Vallet B, Angelini E, Laine A (2006) Variational segmentation framework in prolate
spheroidal coordinates for 3D real-time echocardiography. 11–16 Feb.; San Diego, CA, USA.
p 61444A.61441-61411
23. Hunter PJ, Smaill BH (1988) The analysis of cardiac function: a continuum approach. Progress
in Biophysics and Molecular Biology 52:101–164
24. Christie GR, Bullivant DP, Blackett SA, Hunter PJ (2004) Modelling and visualising the heart.
Computing and Visualization in Science 4(4):227–235
25. Nielsen PM, LeGrice IJ, Smaill BH, Hunter PJ (1991) Mathematical model of geometry
and fibrous structure of the heart. American Journal of Physiology - Heart and Circulation
Physiology 260:H1365–H1378
26. Angelini ED, Song T, Mensh BD, Laine AF. Brain MRI segmentation with multiphase
minimal partitioning: A comparative study. International Journal of Biomedical Imaging
2006;2007(Article ID 10526):1–15
27. Zeng X, Staib LH, Schultz RT, Duncan JS (1999) Segmentation and measurement of the cortex
from 3-D MR images using coupled-surfaces propagation. IEEE Transactions on Medical
Imaging 18(10):927–937
Real-Time 4D Cardiac Segmentation by Active Geometric Functions 253
28. Jin Y, Angelini E, Laine A (2004) Wavelets in medical image processing: Denoising,
segmentation, and registration. In: Suri J, Wilson DL, Laximinarayan S(eds) Handbook of
medical image analysis: Advanced segmentation and registration models. New York NY:
Kluwer Academic Publishers
29. Udupa JK, Samarasekera S (1996) Fuzzy connectedness and object definition: Theory,
algorithms, and applications in image segmentation. Graphical Models and Image Processing
58(3):246–261
30. Pai V, Axel L, Kellman P (2005) Phase Train Approach for very high temporal resolution
cardiac imaging. J Cardiovasc Magn Reson 7(1):98–99
31. Duan Q, Moses D, Srichai MB, Pai VM, Laine AF (2006) Semi-automatic ventricular border
segmentation package based on multi-phase levelset segmentation. Seattle, WA, USA
Qi Duan received the BS and MS
degress in Biomedical Engineering from
Tsinghua University, Beijing, China,
in 2000 and 2002, respectively. He
obtained the PhD degree in Biomedical
Engineering (bioimaging) from the
Columbia University, New York, USA,
in 2007. Between 2008 and 2010, he
was a research scientist in the Center
for Biomedical Imaging, Department of
Radiology, NYU School of Medicine,
New York, USA. Since July 2010, he is
a staff scientist at NINDS, NIH, USA.
His current interests are medical image analysis, MR RF engineering, MR
reconstruction methods, cardiac 4D ultrasound segmentation, and strain imaging.
Classification and Staging of Chronic Liver
Disease Based on Ultrasound, Laboratorial,
and Clinical Data
Ricardo Ribeiro, Rui Tato Marinho, Jasjit S. Suri, and Jo˜ao Miguel Sanches
Abstract Chronic liver disease is a progressive disease, most of the time asymp-
tomatic, and potentially fatal. In this chapter, an automatic procedure to stage the
disease is proposed based on ultrasound (US) liver images, clinical and laboratorial
data.
A new hierarchical classification and feature selection (FS) approach, inspired in
the current diagnosis procedure used in the clinical practice, here called Clinical-
Based Classifier (CBC), is described. The classification procedure follows the wellestablished strategy of liver disease differential diagnosis. The decisions are taken
with different classifiers by using different features optimized to the particular task
for which they were designed. It is shown that the CBC method outperforms the
traditional one against all (OAA) method because it take into account the natural
evolution of the hepatic disease. Different specific features are used to detect and
classify different stages of the liver disease as it happens in the classical diagnosis
performed by the medical doctors.
R. Ribeiro ()
Institute for Systems and Robotics and Instituto Superior T´ecnico/Technical University of Lisbon,
Lisbon, Portugal
Escola Superior de Tecnologia da Sa´ude de Lisboa, Lisbon, Portugal
e-mail: ricardo.ribeiro@estesl.ipl.pt
R.T. Marinho
Liver Unit, Department of Gastroenterology and Hepatology, Hospital de Santa Maria,
Medical School of Lisbon, Lisbon, Portugal
e-mail: rui.marinho@mail.telepac.pt
J.S. Suri
Biomedical Technologies, Inc., Denver, CO, USA
Idaho State University (Affiliated), Pocatello, ID, USA
e-mail: jsuri@comcast.net
J.M. Sanches
Institute for Systems and Robotics, Department of Bioengineering from the Instituto Superior
T´ecnico/Technical University of Lisbon, Portugal
e-mail: jmrs@ist.utl.pt
J.M. Sanches et al. (eds.), Ultrasound Imaging: Advances and Applications,
DOI 10.1007/978-1-4614-1180-2
11, © Springer Science+Business Media, LLC 2012
255
256 R. Ribeiro et al.
The proposed method uses multi-modal features, extracted from US images,
laboratorial and clinical data, that are known to be more appropriated according to
the disease stage we want to detect. Therefore, a battery of classifiers and features
are optimized and used in a hierarchical approach in order to increase the accuracy
of the classifier.
For the normalclass we achieved100% accuracy, for the chronic hepatitis 69.2%,
for compensated cirrhosis 81.48%, and for decompensated cirrhosis 91.7%.
1 Introduction
Chronic liver disease (CLD) is a significant cause of morbidity and mortality
in developed countries and commonly is caused by viral hepatitis and alcohol
abuse [1].
The initial stages of CLD are usually asymptomatic such as steatosis or hepatitis.
Hepatitis is the inflammation of the liver, resulting in liver cell damage and
destruction [1]. It is caused by hepatitis viruses, which can have several types, or by
other factors, e.g., alcohol. Moreover the natural evolution of the disease may lead
to cirrhosis or even hepatocellular carcinoma, which are more severe pathological
conditions, with high morbidity and mortality. Cirrhosis is a chronic disease that
is characterized anatomically by widespread nodules in the liver combined with
fibrosis [2]. It is possible to distinguish two phases in cirrhosis, a stable form, called
compensated cirrhosis, and a more dangerous from that could lead to widespread
failure of the liver, called decompensated cirrhosis [3].
Liver biopsy has been the preferred tool in the evaluation and staging of the
CLD. However, its invasive nature and the development of other more accurate noninvasive alternatives have lead to a decrease on its usage for assessing the CLD.
Among these alternatives, CLD staging based on ultrasound (US) data has proven
to be a promising and safer alternative to biopsy.
In the review study presented in [1] it is shown that echogenicity, texture
characterization, and surface morphology of the liver parenchyma are effective
features to diagnose the CLD. However, the evaluation of these features is normally
affected by the subjective assessment of the human operator. This factor may lead
to significant errors in the diagnosis and staging of CLD, since US liver images
can show great variability, as shown in Fig. 1. Therefore, new objective feature
extraction and classification methodologies in a Computer Assisted Diagnosis
framework are needed.
Several studies presented in the literature use objective features, extracted from
US images, and propose classification procedures to assess CLD [4]. Some of the
most common features are based on the first-order statistics, co-occurrence matrix,
wavelet transform, attenuation and backscattering parameters and coefficients.
A brief description of some of these studies is given next.
In [5], an experimental study was performed aiming at to discriminate the liver
fibrosis from US images. They computed fractal features, entropy measures, and
Classification and Staging of Chronic Liver Disease... 257
Fig. 1 Ultrasound images variability in the different stages of chronic liver disease
co-occurrence information from US images to characterize the liver parenchyma
from a textural point of view and the classification results showed an overall
accuracy (OA) of 85.2% using a Fisher linear classifier. Other important work
described in [6] shows the ability of the Wavelet coefficients, also computed from
US images, to characterize the diffuse disease of the liver. Their goal was to
discriminate normal, steatotic, and cirrhotic conditions. An OA of 90% is obtained
and comparison results by using other classes of features, such as co-occurrence
information, Fourier descriptors and fractal measures, showing that the waveletbased classifier outperforms the classifiers based on the other features, 87%, 82%,
and 69%, respectively.
Lee et al. [7] categorized patient in normal(72), fatty liver (66), and CLD (64),in
order to evaluate the usefulness of standard deviation to measure the homogeneity
of hepatic parenchyma based on US images. They observe significant differences
(p< 0.0001) between the CLD group and the normal and fatty liver groups. They
also concluded that higher average standard deviation values are related to wide
distribution of intensity values within the ROI, as reported for CLD group, which
explainsthe characteristicappearance of heterogeneousecho texturein CLD groups,
such as chronic hepatitis and liver cirrhosis. Depict the good results obtained, the
authors suggest to be careful in the use of this feature, since it is highly dependent
on the ROI location.
258 R. Ribeiro et al.
Two main contributions for the CLD assessment are presented in this work;
(1) multi-modal features, extracted from US images, laboratorial and clinical data
and (2) a new classification procedure inspired in the clinical practice, here called
Clinical-Based Classifier (CBC).
The discriminative power of the automatic classifier can be greatly increased if
the natural evolution and staging of the disease is taken into account.
The remainder of this chapter is organized as follows: Section 2 introduces
the pre-processing algorithm used, explaining the feature extraction and selection
procedures, as well as the classifiers and the dataset used in this work. In Sect. 3 the
results are presented showing the feature selection (FS) results and the classification
results for each of the used classifier. The discussion of the results is presented in
Sect. 4 and conclusions are presented in Sect. 5.
2 Methods
The CBC aims at discriminating normal and three main pathologies in the CLD
scope; (1) Chronic Hepatitis,(2)Compensated Cirrhosis,and(3)Decompensated
Cirrhosis.
The diagnosis of these pathologies is performed in the today clinical practice
based on several sources of medical data such as US liver parenchyma images,
laboratorial exams, and clinical indicators recommended in well established and
accepted medical guidelines [3]. The diagnosis, however, is obtained by integrating
all information based mainly on subjective criteria of the medical doctor.
The CBC is a quantitative and highly automatic procedure that gives the medical
doctor objective and accurate information to help in the liver diagnosis process.
The CBC approach is composed by three main components; (1) Features
computation from multi-modal sources, (2) design and training of a specific suitable
classification strategy that takesinto account the CLD specificities, and (3) diagnosis
and validation of the method.
The main novelty of the method proposed in this chapter is a hierarchical
classifier that mimics the structural approach of differential diagnosis followed in
the clinical practice to identify the different stages of the CLD [8]. Instead of trying
to classify a given liver in one stage of the disease from a set of possible stages
by using a multi-class classifier, e.g., k-Nearest Neighbor (kNN) or Support Vector
Machine (SVM), the hierarchical approach, represented in Fig. 2, is used. In this
strategy several partial binary decisions are taken according to the natural evolution
of the disease. In each step, a decision is taken by differentbinary classifiers trained,
tunned, and optimized specifically for that task.
The first classification step (CS) discriminates normal versus pathology liver.
If the liver is classified as pathologic in this first step, discrimination of chronic
hepatitis without cirrhosis versu cirrhosis is attempted. In the last step compensated
cirrhosis versu decompensatedcirrhosis are discriminated. The decompensated cir-
rhosis is assumed as the end-stage of every CLD before hepatocellular carcinoma.
Classification and Staging of Chronic Liver Disease... 259
Fig. 2 Design of the CBC decomposition strategy for CLD classification
The CBC (see Fig. 2) design and optimization is performed at two levels: (1)
features and (2) classifier type and parametrization selection used specifically in
each CS. This means that at each CS of our hierarchical approach the classifier type
and features can be different.
The FS procedure is formulated as an optimization task with the sensitivity
maximization criterion [9, 10]. The set of features at each CS is tunned for the
specificities of the corresponding CLD stage prior to the classifier type selection.
This is done by the sequential forward floating selection (SFFS) [11] method with
the linear discriminant analysis (LDA) criterion. The leave-one-out cross-validation
technique is used for error estimation purposes.
The classifier selection at each CS is done with ROC analysis where the selected
classifier, kNN or SVM [12], is the one that jointly maximizes the true positive rate
(TPR) and the true negative rate (TNR).
The non-parametric kNN classifier is tested in this study. It classifies a test
sample to a class according to the majority of the training neighbors in the feature
space by using the minimumEuclidean distance criterion [13,14]. The algorithm for