Computerized Segmentation and Classification of Breast Lesions Using Perfusion Volume Fractions in Dynamic Contrast-enhanced MRI

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1 28 International Conference on BioMedical Engineering and Informatics Computerized Segmentation and Classification of Breast Lesions Using Perfusion Volume Fractions in Dynamic Contrast-enhanced MRI Sang Ho Lee 1,3, Jong Hyo Kim 1,2,3, Jeong Seon Park 2, Jung Min Chang 2, Sang Joon Park 1,3, Yun Sub Jung 1,3, Woo Kyung Moon 2 1 Interdisciplinary Programs in Radiation Applied Life Science major, 2 Department of Radiology, 3 Institute of Radiation Medicine, Seoul National University College of Medicine, Seoul, Korea lsho76@snu.ac.kr Abstract This study is designed to segment suspicious regions using automatic computerized procedures and to classify kinetic patterns using commercially available three-time-points (3TP) method of computeraided diagnosis. A novel evaluation method using perfusion volume fractions is introduced for examining meaningful kinetic features in differentiation of benign and malignant breast lesions. Dynamic contrastenhanced MRI was applied to 24 lesions (12 malignant, 12 benign). Thresholding for suspicious regions, region growing segmentation, hole-filling and 3D morphological erosion and dilation were performed for extracting final lesion volume. The lesion sphericity and center distance of mass to surface area ratio (CDMSAR) were considered in the process of automatic segmentation. The kinetic patterns for each lesion were classified into six classes by the 3TP method. Perfusion volume fraction for each class was calculated in three partitions of whole, rim and core volumes of a lesion. Receiver operating characteristic curve (ROC) analysis was performed using the perfusion volume fractions. When using perfusion volume fractions divided into rim and core lesion volume, the classes having more improved accuracy appeared than using perfusion volume fractions within whole lesion volume. This result indicates that lesion classification using local perfusion volume fractions is helpful in selecting meaningful kinetic patterns for differentiation of benign and malignant lesions. 1. Introduction Dynamic contrast-enhanced (DCE) breast MRI is being used for detection, diagnosis, and staging of breast cancer, especially for women at high risk [1]. Breast MR acquisitions consist of a pre-contrast baseline scan followed by several post-contrast series at multiple time points (Figure 1). Typically, computer-aided diagnosis (CAD) algorithms have focused on the morphological information, but CAD for breast MR can use an entirely new class of temporal features. This study was attempted to examine a single temporal feature, a time versus percent enhancement curve. Kuhl et al. showed that the use of curve shape (washout, plateau, or persistent enhancement) based on three-time-points (3TP) method could distinguish malignant lesions from benign [2]. Curves that exhibited a washout type behavior after initial enhancement had an 87% probability of being malignant, whereas curves that exhibited persistent enhancement only showed a 6% likelihood of being malignant. Washout behavior of lesions has also been shown to be correlated with tumor angiogenesis and vascular permeability [3-4]. However, other many investigators have found that there was a considerable overlap in the range of enhancement rates of malignant and benign lesions [5-9]. Until now, many investigators have reported mostly the findings on kinetics with existence of washout behavior within a lesion for differentiation of benign and malignant lesions, depending on the user-specific regions of interest. This study introduces a novel approach of automatic segmentation procedure and tumor classification using perfusion volume fractions in order to characterize the vascularity of a lesion volume. This trial is based on hypotheses that there are some critical perfusion volume fractions for differentiation of benign and malignant lesions and that analysis of the local perfusion volume fractions such as tumor rim and/or core is more helpful. This study was attempted to classify contrast enhancement patterns of a lesion /8 $ IEEE DOI 1.119/BMEI

2 Figure 1. Pre-constrast, the first, second, third and last post-contrast images on a malignant lesion. Notice the increase in contrast on the first post-contrast image, followed by the washout of contrast on the last post-contrast image. using the 3TP method and to examine meaningful enhancement patterns having improved accuracy in differentiation of benign and malignant lesions using perfusion volume fractions. 2. Materials and Methods 2.1. Materials and Protocol DCE-MRI was applied to 24 lesions (12 malignant, 12 benign) using 1.5 T Sonata (Siemens, Erlangen, Germany). First, the pre-contrast T1 weighted 3D fast low angle shot (FLASH) sagittal image (TR 4.9 ms, TE 1.83 ms, FA 12, FOV 17 mm, matrix , acquisition time 84 s, slice thickness mm without gap) was obtained with fat suppression and next four consecutive post-contrast images using the same condition after an injection of.1 mmol/kg Gd-DTPA (Magnevist, Schering, Berlin, Germany). The contrast material was administered manually at a flow rate of 2 ml/s for 5 sec and imaging was performed within 15 sec after injecting the contrast agent Thresholding of Suspicious Regions 3D rigid registration (fixed image, pre-contrast image; moving image, post-contrast images) were performed for reducing the artifacts of patient movement. Initially, we constructed our proposed perfusion index (PI) using both signal intensity and enhancement curves for subsequent extracting suspicious regions by threshold as the following. N N 1 MI PI PEi ( Posti Pre) SERi ( Posti PostN) k i 1 i 1 i where Post Pre PEi and Posti Pre SERi (1) Pre PostN Pre Thresholding of suspicious regions was based on the nonparametric and unsupervised method of automatic threshold selection presented by Otsu [1]. Table 1. Notations used in the expression (1) MI The maximum intensity value among all postcontrast series k The series number of the maximum intensity phase PE i Percent enhancement of the ith series SER i Signal enhancement ratio of the ith series Post i The ith post-contrast image Post N The last post-contrast image Pre Pre-contrast image N The total number of post-contrast series 2.3. Lesion Segmentation The connected threshold region growing segmentation was performed in order to extract a connected component from a user-defined seed on the threshold image, and hole-filling on its segmented region. Sphericity (S) and center distance of mass to surface area ratio (CDMSAR) of the threshold image were used for deciding whether the segmented volume should be eroded or not, and the number of erosion, if the segmented volume should be erode, by the following expressions p 3 (6 V ) S (2) Ap CDMSAR 1 2 M CM Pi i 1 p M A (3) Where V p, A p, CM, P and M are total volume of the segmented particles (or voxels), surface area, center of mass, each segmented particle and total number of the segmented particles, respectively. If erosion was performed repeatedly on the segmented volume, the connected threshold region growing segmentation followed each erosion and dilation was executed equally to the frequency of the erosion. Also, dilation was performed in the range above the median value

3 between maximum and mean values of PI within dilation volume in order to compensate speculate lesion boundary removed by erosion. Finally, erosion was performed until erosion volume was under 1/3 of the final lesion volume for dividing the lesion rim and core volumes TP Classification The enhancement curves for each lesion were classified based on the criteria of the 3TP method. The enhancement properties were quantified by computing the PE 1 and SER 1 (Figure 2). Figure 2. Voxel classification scheme based on PE and SER values 2.5. Volume Measurement Each whole lesion volume was calculated by voxel counting from the segmentation outcome. Intratumoralperfusion volume fractions of the classes which are resulted from the 3TP classification were computed from whole, rim and core volumes for each lesion Receiver Operating Characteristic Curve (ROC) Analysis ROC analysis was performed with the perfusion volume fraction for each class using MedCalc statistical software version (Belgium) in order to evaluate performance for differentiation of benign and malignant lesions. The results of ROC analyses using whole, rim and core perfusion volumes of all lesions were compared one another. 3. Results The classification results were shown within a lesion volume by different colors reflecting each enhancement pattern. Homogeneity or heterogeneity of the enhancement patterns within the lesion volume was able to be visually identified and perfusion volume fraction for each color confirmed (Figure 5). When using perfusion volume fractions in whole lesion volume, the class 1 (moderate uptake and washout; 5% PE<1% and SER>1.1) showed: sensitivity, 83.3%; specificity, 5%; and accuracy, 61.1%, class 2 (moderate uptake and plateau; 5% PE<1% and.9<ser<1.1): sensitivity, 83.3%; specificity, 58.3%; and accuracy 67%, class 3 (moderate uptake and persistent; 5% PE<1% and SER<.9): sensitivity, 91.7%; specificity, 5%; and accuracy, 58.3%, class 4 (rapid uptake and washout; PE 1% and SER>1.1): sensitivity, 91.7%; specificity, 58.3%; and accuracy, 77.1%, class 5 (rapid uptake and plateau; PE 1% and.9<ser<1.1): sensitivity, 75%; specificity, 91.7%; and accuracy, 83.3%, and class 6 (rapid uptake and persistent; PE 1% and SER<.9): sensitivity, 91.7%; specificity, 33.3%; and accuracy, 52.8% (Figure 3a). In rim lesion volume, class 1 showed: sensitivity, 1%; specificity, 25%; and accuracy, 59%, (a) (b) (c) Figure 3. Comparisons of ROC curves of classes on (a) whole, (b) rim and (c) core lesion volumes 984

4 Lesion Means of perfusion volume fractions in whole lesion volume (error bars: 95% CI for mean).7 Means of perfusion volume fractions in rim lesion volume (error bars: 95% CI for mean).6 Means of perfusion volume fractions in core lesion volume (error bars: 95% CI for mean) Lesion Lesion (a) (b) (c) Figure 4. Means of perfusion volume fractions in (a) whole, (b) rim and (c) core volumes of benign and malignant lesions class 2: sensitivity, 91.7%; specificity, 5%; and accuracy, 65.6%, class 3: sensitivity, 83.3%; specificity, 5%; and accuracy, 61.1%, class 4: sensitivity, 66.7%; specificity, 83.3%; accuracy, 76.4%; class 5: sensitivity, 1%; specificity, 5%; and accuracy, 75.7%, and class 6: sensitivity, 83.3%; specificity, 5%; and accuracy, 57.6% (Figure 3b). In core lesion volume, class 1: showed sensitivity, 83.3%; specificity, 83.3%; and accuracy 82.6%, class 2: sensitivity, 83.3%; specificity, 75%; and accuracy 72.9%, class 3: sensitivity, 1%; specificity, 33.3%; and accuracy, 5%, class 4: sensitivity, 83.3%; specificity, 66.7%; and accuracy, 72.9%, class 5: sensitivity, 1%; specificity, 58.3%; and accuracy, 79.2%, and class 6: sensitivity, 41.7%; specificity, 83.3%; and accuracy, 61.1% (Figure 3c). When using perfusion volume fractions divided into rim and core lesion volume, the classes having more improved sensitivity or specificity for differentiation of lesions appeared than using perfusion volume fractions in whole lesion volume. Figure 4 shows the means of perfusion volume fractions in whole, rim and core volumes of benign and malignant lesions. Perfusion volume fractions in whole volume show that malignant lesions are higher in class 1, 2, 4 and 5 than benign lesions, and in rim volume also similar tendency. On the other hand, perfusion volume fractions in core volume show that malignant lesions are higher in class 1, 2, 4 and 5 than benign lesions similarly in whole and rim volumes, but malignant lesions are higher in class 6 than benign lesions. This means that the rapid uptake and persistent curve pattern of class 6 is relatively more concentrated in core volume of malignant lesions than in that of benign lesions. 4. Discussion This study provides new insight which verifies volumetric distributions of enhancement patterns within a lesion. Although commercial softwares such as CADstream TM (Confirma Inc., USA) and DynaCAD (Invivo Co., USA) for breast MRI CAD have already provided information on perfusion volume fractions, the studies using perfusion volume fractions for differentiation of benign and malignant lesions have not been reported. The appropriate decision of initial lesion segmentation method is important for obtaining accurate perfusion volume fractions because the perfusion volume fraction of each class can be over- or underestimated, depending on segmentation method. Current commercial softwares do not still give an acceptable solution for lesion segmentation on breast DCE-MRI and they are designed to perform only simple curve thresholding for detection of suspicious regions. The simple curve thresholding method is not completely able to remove blood vessel components around a lesion or chest organic artifacts. Thus, we constructed that the difference of kinetic features between a lesion and normal tissue components was maximized by PI and were able to achieve more effectively thresholding of suspicious regions by Otsu threshold method, which finds an optimal threshold that separates the two main classes of an image, background and foreground. In addition, the blood vessel components having lesion-like enhancement curves were able to be eliminated by connected threshold region growing segmentation and morphological operation (erosion and dilation). Kuhl et al. using the 3TP method have reported that the diagnostic efficacy of pattern evaluation of signal intensity time curves and enhancement rate was sensitivity, 91%; specificity, 83-37% [2]. This was the result which analyzed distribution of curve types based on user-specific ROIs. On the other hand, our study uses the volume fraction of each curve type on a voxelby-voxel basis. The perfusion volume fractions depend

5 (a) (b) Figure 5. Examples of color maps of enhancement patterns assigned by the 3TP method and perfusion volume fractions in the whole volume of (a) malignant and (b) benign lesions on the result of lesion segmentation, but our results show possibility to surpass existing results. In addition, this study suggests that using perfusion volume fractions which are divided into rim and core volumes of a lesion was advantageous to diagnostic accuracy, considering the characteristic which malignant lesions have usually distinct rim enhancement. In the future, it is necessary to demonstrate the usefulness of perfusion volume fractions after collecting more patient data. 5. Conclusion This result indicates that lesion classification using local perfusion volume fractions is helpful in selecting meaningful kinetic patterns for differentiation of benign and malignant lesions. 6. References [1] S. G. Orel, and M. D. Schnall, MR Imaging of the Breast for the Detection, Diagnosis, and Staging of Breast Cancer, Radiology, vol. 22, 21, pp [2] C. K. Kuhl, P. Mielcareck, S. Klaschik, C. Leutner, E. Wardelmann, J. Gieseke, and H. H. Schild, Dynamic Breast Imaging: Are Signal Intensity Time Course Data Useful for Differential Diagnosis of Enhancing Lesions?, Radiology, vol. 211, 1999, pp [3] M. Knopp, E. Weiss, H. Sinn, J. Mattern, H. Junkermann, J. Radeleff, A. Magner, G. Brix, S. Delorme, I. Zuna, and G. van Kaick, Pathophysiologic Basis of Contrast Enhancement in Breast Tumors, Journal of Magnetic Resonance Imaging, vol. 1, 1999, pp [4] L. Esserman, N. Hylton, T. George and N. Weidner, Contrast-Enhanced Magnetic Resonance Imaging to Assess Tumor Histopathology and Angiogenesis in Breast Carcinoma, The Breast Journal, vol. 5, no. 1, 1999, pp [5] S. H. Heywang, A. Wolf, E. Pruss, T. Hilbertz, W. Eiermann, and W. Permanetter, MR imaging of the breast with Gd-DTPA: use and limitations, Radiology, vol. 187, 1993, pp [6] R. Gilles, J. M. Guinebretiere, O. Lucidarme, P. Cluzel, G. Janaud, J. F. Finet, A. Tardivon, J. Masselot, and D. Vanel, Nonpalpable breast tumors : diagnosis with contrastenhanced subtraction dynamic MR imaging, Radiology, vol. 191, 1994, pp [7] P. C. Stomper, S. Herman, D. L. Klippenstein, J. S. Winston, S. B. Edge, M. A. Arredondo, R. V. Mazurchuk, and L. E. Blumenson, Suspect breast lesions: findings at dynamic gadolinium-enhanced MR imaging correlated with mammographic and pathologic features, Radiology, vol. 197, 1995, pp [8] E. S. Fobben, C. Z. Rubin, L. Kalisher, A. G. Dembner, M. H. Seltzer, and E. J. Santoro, Breast MR imaging with commercially available techniques: radiologic-pathologic correlation, Radiology, vol. 196, 1995, pp [9] S. G. Orel, M. D. Schnall, V. A. LiVolsi, and R. H. Troupin, Suspicious breast lesions: MR imaging with radiologic-pathologic correlation, Radiology, vol. 19, 1994, pp [1] N. Otsu, A Threshold Selection Method from Gray- Level Histograms, IEEE Transactions on Systems Man and Cybernetics, vol. SMC-9, No. 1, 1997, pp