Yongqiang Zhao Feng Hong Minglu LI. Department of Computer Science & Engineering. Shanghai Jiaotong University Shanghai. China. Ihrrr-ocl- This paper ...
MULTIMODALITY MRI INFORMATION FUSION FOR OSTEOSARCOMA SEGMENTATION Yongqiang Zhao Feng Hong Minglu LI Department of Computer Science & Engineering Shanghai Jiaotong University Shanghai. China Ihrrr-ocl- This paper prcrenls a novel system l o improve
osteorarcoma segmentation based on the information fusion. Conventional and dynamic contrart-enhanced MRI sequences can be treated as multiple sensors. This system aims l o give the correct extension of tumor for surgical planning and resection. Keywords - MRI, segmentation, information fusion 1. INTRODUCTION
Osteosarcoma is the most common primary malignant tumor of bone. Magnetic resonance imaging (MRI), being noninvasive and providing excellent soft tissue contrast, has been proved to be superior to other imaging tools in staging osteosarcoma. Moreover, segmentation of MRI is an important step in medical image processing, as it is aimed, to be identification of anatomical regions of interest for diagnosis, treamient or surgical planning. However, the diagnostic accuracy of conventional MRI in evaluating tumor extent remains controversial because it may not be able to distinguish microscopic tumor invasion from inflammatory reaction surrounding tumor. Dynamic MRI may be able to overcome this disadvantage, since it has been successfully applied to tissue that conventional MRI can nor properly assess, such as differentiating malignant tumor from benign tumor and evaluating the viability of malignant neoplasms after chemotherapy[l]. Fusion of information from dynamic MRI sequence is expected to yield results that constitute an improvement over those obtained from standard segmentation procedures applied to the conventional MRI sequences.
After segmenting three sequences respectiyely, different results were obtained as different character of the same tissue under three conditions. B. Dj.naiiiic MRl a17d anali~sisof rime-inreiisin~c u i w Dynamic MRI with contrast agents is a very promising technique for studying tissue perfusion in vivo. A temporal series of MRI of the same slice are acquired following the injection of a contrast agent into the blood stream[?]. To analyze time-intensity curves for regions of interest, we use the two following parameters: a) Similarity Mapping: it identifies regions in a dynamic maze sequence according to their temporal similarity or issimilarity with respect to a reference ROI. From some study, the best results to date were obtained using normalized cross-correlation as a measure of similarity. The similarity maps NCOR based on normalized correlation have alues in the range from -1 to + I . Regions which are identical have a correlation of + I . and regions that are different have a correlation equal to zero. Regions with correlation equal to -1 are perfect “negatives” of the reference sequence[3]. NCOR
b) Slope Value: In the present study dynamic MRI was successfully utilized to distinguish the zone of microscopic tumor from macroscopic tumor andor tumor-free marrow. based on the assessment of slope value. The steepest slope value of the time-intensity curve was calculated on the basis of the first-pass method on dynamic MRI.
11. METHODOLOGY A . biirial Segmentalion
According to the different intention. there are mainly three kinds of conventional MRI sequences for osteosarcoma diagnosis. (1)Tl WI: TI-weighted images(Fig2.a) show fine anatomic structure, and display the relationship between the tumor tissue and the surrounded nerve and blood vessel clearly. Most osteosarcoma had same or lower signal intensity than musculature, or had high and low simal intensity mixed when bleeding and putrescence. (2) On R-weighted spin echo images (FigZ.b), the tumor had higher and more heterogeneous signal than the focal area of concern in the marrow. T2-Weighted MRI showed the relatively high signal intensity, corresponding to the radiolucency, surrounded by a very low’signal intensity area. (3) STIR(Short Tau Inversion Recovery): This kind of osteosarcoma MR images(Fig2.c) is the effective method to distinguish noniial and abnormal marrow exactly. As the fat signal is repressive, the tumor had obviously high intensity contrasting with the IOU’ intensity of medulla.
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Differences were significant for macroscopic tunior, zone of microscopic invasion and tumor-free marrow [I]. C. lnforniarionftnion We can get two types of information, from the conventional MRI sequences, we called the anatomic information: from the dynamic MRI, called numerical information, which mainly evaluates tumor extension. Each slice in MRI sequeiices consists of K objects representing K distinct tissues defined by 6 = {tumor ,i~nasion (3) ntai~row ,bone , fat ,niiiscle , others } We aimed at finding the exact segmentation of these tissues, and me were particularly interested in tumor, invasion and marrow distributions. Our fusion architecture is given Fig.1 i n which the fusion system exploits initial segmentation of three
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ACKNOWLEDGMEKT This research is supported by a grant from Hi-Tech Research and Development Program of China(grant #2001AAI 14150), and Dawnin:: Program of Shanghai, China(gran1 #OZSGI 5).
REFERENCES T2
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STIR
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NCOR
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Figure. 1 the architecture of the information fusion system. More precisely, we considered the fusion as an aggregation of ambiguous, conflicting, complementaly and redundant information, in order to provide a more accurate and less uncertain classification of tissues. We considered the following three-step fusion scheme: I ) After we get three initial segmentation results of TI, T2 and STIR, we model the obtained information with 3D maps; 2) Aggregate 3 0 maps data and numerical information, according to their redundancy, complementary and ambiguity. The aggregation was performed by taking into account the asreement or the conflict among the memberships of tissues; 3) A proposal for a segmentation is suggested to the doctor, which includes the fact that we cannot decide or if conflict between the source of information.
[I]T. Iwasawa, Y. Tanaka, and N. Aida. Microscopic intraosseous extension of osteosarcoma: assessment on dynamic contrastenhanced mri. S k e i ~ mRndiol. i ?6(4):214-221. April 1997. 121 1. Ro_eowska,K. Preston. and G. 1.Hunter. Applications of similarity mappin8 in dynamic mn. I . ~ E E . T r a n s . ~ 4 ~ d . l n i ' ~ ~ . . 14(3):48&486. Sep. 1995. [3] G. Sebastiani,F. Godtliebsen,and R. AJone:;. Analysis of dynamic magnelic resonance images. IEEE. Trons.Aed.lmag.. 15(3):268-277, June 1996. [4] B. Solaiman, R. Debon, F. Pipelier, J.M. Cauvin and C. Roux, Information Fusion: Application to Data and Model Fusion for Ultrasound Image Segmentation. IEEE. Trn~s. Med. fnia:.,46(lO):ll7I-l 175, Oct. 1999. [ 5 ] Vincent Barra, Jean-Yves Boire, Automatic Segmentation of Subcortical Brain Structures in MR
111. RESULTS and DISCUSSION
The MR images were provided by Tongji Hospital Affiliate to Tongji University Fig.2d shows the result that combines the segnentation information after initial segmenting Fig.2 a, b and c. The black section in leg displayed the pathologic area. But it does not the relation ship of tumor, invasion and mmow. Table1 shows the slope value of seven points (clockwise order) in Fig.3. From the conclusion of [l], P2, P3 and P7 belong to tumor, P3 and P6 are invasion, others belong to marrow. The information from the time-intensity cuwe analysis will help in refinement of initial 3D segmentation result.
Figure2
slice of (a) TI (b) T2 (c)'STIR sequences (d) segmentation result
IV. COI\'CLUSION
conventional and dynamic MRl sequences. Actual studies are conducted in order to generalize the proposed information fusion system to the case of fully three
point slope Point
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