223 ノート Code No. 500 立永 MR 1 謹 2 日浦友樹 3 川下郁生 1 大倉保彦 石田隆行 緒言 multiple sclerosis: MS MS 1 MR MS 2, 3 MR MS Development of a
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1 223 ノート Code No. 500 立永 MR 1 謹 2 日浦友樹 3 川下郁生 1 大倉保彦 石田隆行 緒言 multiple sclerosis: MS MS 1 MR MS 2, 3 MR MS Development of a Computer-aided Diagnostic System for Detecting Multiple Sclerosis Using Magnetic Resonance Images Susumu Tachinaga, 1 Yuuki Hiura, 2 Ikuo Kawashita, 3 Yasuhiko Okura, 1 and Takayuki Ishida 4 1 Major in Medical Engineering and Technology, Integrated Human Sciences, Graduate School of Hiroshima International University 2 Chugoku Rousai Hospital 3 Department of Clinical Radiology, Faculty of Health Sciences, Hiroshima International University 4 Division of Health Sciences, Osaka University Graduate School of Medicine Received July 4, 2013; Revision accepted January 13, 2014 Code No. 500 Summary It is of key importance to be able to evaluate the temporal changes seen in multiple sclerosis (MS) lesions in terms of location, shape, and area for estimating MS progression. The purpose of our study was to develop an automated method for detecting potential MS regions based on three types of brain magnetic resonance (MR) images: T 1 - and T 2 -weighted images, and fluid attenuated inversion-recovery (FLAIR) images. The brain regions were segmented based on a tri-linear interpolation technique and k-mean clustering technique. True positive regions and false positive regions were classified from three types of MR images using a support vector machine (SVM). We applied our proposed method to 60 slices of 20 MS cases. As a result, the sensitivity for detection of MS regions was 81.8%, with 14.1% false positives per true positive. This method should prove useful for the diagnosis of multiple sclerosis. Key words: multiple sclerosis, computer-aided diagnosis (CAD), magnetic resonance imaging (MRI), k-mean clustering technique *Proceeding author
2 224 a b c Fig. 1 Original image. (a) FLAIR image, (b) T 1-weighted image, (c) T 2-weighted image MR MS , 10 Fig. 1 T 1 T 2 FLAIR SVM MS 1. 使用画像データベース Medical Image Computing and Computer Assisted Intervention MICCAI 2008 Work Shop MS Lesion Segmentation 20 CHB: the Children s Hospital Boston SIGNA EXCITE 1.5 T GE MEDICAL SYSTEM 10 UNC: the University of North Carolina ALLEGRA 3.0 T SIEMENS bit mm MS MS mm T 1 T 2 FLAIR DTI_FA: the fractional anisotropy map DTI_MD: the mean diffusivity map MS Fig. 2 Flow chart of the scheme. MR 3 FLAIR T 1 T T MRI T 2. 方法 Fig FLAIR T 1 T 2 k Fig. 3
3 225 Fig. 3 Flow chart of brain segmentation. FLAIR 12 2 Fig MR B y = T x+ B B T T 1 xy B standard deviation: SD T B T FLAIR Fig. 4 Fig. 5 T 1 T 2 Fig MS FLAIR T 1 T 2 support vector machine SVM 13 MS Segmentation results using our method. (a) Coronal image, (b) Axial image, (c) Sagittal image, (d) Volume-rendering image MS 2-5 MS R e1071 SVM MS a c b d
4 226 Fig. 5 Histogram of gray level of corrected FLAIR image. (a) Standard image, (b) Before and after gray level correction a b c Fig. 6 Before and after gray level correction. (a) Basis image, (b) Before gray level correction image, (c) After gray level correction image Fig. 7 SVM FLAIR T 1 T 2 positive negative SVM linear polynomial radial sigmoid 4 radial gamma 1 cost Consistency test Validation test Validation test leave-one-patient-out
5 T 3.0 T MRI 1.5 T T T T T T T 3.0 T MS MS MS MS 3. 結果と考察 T Consistency test Validation test Consistency test Validation test Consistency test Fig. 7 Method of distinguishing between normal and MS regions. Case 5 MS Fig. 8 Fig. 8 true-positive: TPfalse-negative: FN true-negative: TN false-positive: FP Table 1 Consistency test Validation test MS MS T MRI T MRI T MRI T MRI
6 228 Table 1 Relationship between sensitivity and volume of false positives using 1.5 T MRI Fig. 8 Original images and discriminant result images (1.5 T MRI). (a) FLAIR image, (b) T 1-weighted image, (c) T 2-weighted image, (d) True MS regions determined by radiologist, (e) Discriminant result image Sensitivity Volume of (%) falsepositive (%) Consistency Case test Case Case Case Case Case Case Case Case Case Validation Case test Case Case Case Case Case Case Case Case Case Fig. 9 Original images and discriminant result images (3.0 T MRI). (a) FLAIR image, (b) T 1-weighted image, (c) T 2-weighted image, (d) True MS regions determined by radiologist, (e) Discriminant result image 11.5 Validation test T 3.0 T MRI 3.0 T MRI 1.5 T MRI 3.0 T MRI Case 5 MS Fig. 9 Fig. 9 Table 2 Consistency test adaptive mixtures method AMM Markov random field MRF model K-nearest neighbor KNN classification technique MS MS MS MS
7 229 Table 2 Relationship between sensitivity and volume of false positive using 3.0 T MRI Sensitivity Volume of false- (%) positive (%) Consistency Case test Case Case Case Case Case Case Case Case Case Validation Case test Case Case Case Case Case Case Case Case Case SVM 4. 結論 MR MS MS 参考文献 1 i.htm 2013/3/30 2 Goldberg-Zimring D, Achiron A, Guttmann CR, et al. Three-dimensional analysis of the geometry of individual multiple sclerosis lesions: detection of shape changes over time using spherical harmonics. J Magn Reson Imaging 2003; 18(3): Guttmann CR, Kikinis R, Anderson MC, et al. Quantitative follow-up of patients with multiple sclerosis using MRI: reproducibility. J Magn Reson Imaging 1999; 9(4): Khayati R, Vafadust M, Towhidkhah F, et al. A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images. Comput Med Imaging Graph 2008; 32(2): Anbeek P, Vincken KL, van Osch MJ, et al. Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 2004; 21(3): Anbeek P, Vincken KL, van Osch MJ, et al. Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal 2004; 8(3): Raff U, Rojas GM, Hutchinson M, et al. Quantitation of T2 lesion load in patients with multiple sclerosis: a novel semiautomated segmentation technique. Acad Radiol 2000; 7(4): Van Leemput K, Maes F, Vandermeulen D, et al. Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging 2001; 20(8): Yamamoto D, Arimura H, Kakeda S, et al. Development of automated method for detection of multiple sclerosis candidate regions based on brain magnetic resonance images, MI 2007; 107(220): Lao Z, Shen D, Liu D, et al. Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Acad Radiol 2008; 15(3): Bradley PS, Fayyad UM. Refining initial points for k-means clustering. Proceedings of the 15th International Conference on Machine Learning. 1998; Dougherty RP, Kunzelmann K-H. Computing Local Thickness of 3D Structures with ImageJ. Microscopy & Microanalysis 2007 Meeting
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