158 MEDICAL IMAGING TECHNOLOGY Vol. 35 No. 3 May 2017 * * PET/CT 1 1 2 2 3 2 4 PET/CT PET/CT CT PET CT 10 PET/CT FROC 1 3.9 76 PET/CT Med Imag Tech 35 3 : 158 166, 2017 1. 1 magnetic resonance imaging MRI computed tomography CT positron emission tomography/computed tomography PET/CT CT MRI PET PET PET/CT 1 470-1192 1-98 e-mail: teramoto@fujita-hu.ac.jp 2 3 4 2016 5 13 2017 2 8 PET/CT CT PET PET/CT PET CT computer aided diagnosis CAD CAD 2 2 CAD CAD CAD 3
Med Imag Tech Vol. 35 No. 3 May 2017 159 1 CAD 4 PET/CT PET CAD 5, 6 PET CT CAD 7, 8 CT 9, 10 PET/CT CAD PET/CT c 2 a b z c xy 3 2. PET/CT 1 CT PET 1 1 2 B cube z Z B1 Z B2 xy Y B c 2 CT CT L L A L B 11 3 z Th bone CT 3
160 Med Imag Tech Vol. 35 No. 3 May 2017 4 xy a 5 Z B1 3 19 Z B2 L B 4 xy xy 12, 13 Th bone CT 1 1 5 4 y x 4 x sx ex y 4 sx y 4 y Y B Y B xy 5 z Z B1 Z B2 xy 5 B cube T Th breast T A B cube A T L B final 2 1 SUV PET PET standardized uptake value SUV 14 SUV = 2 [Bq/g] [Bq]/ [g] 1 PET 15 2
Med Imag Tech Vol. 35 No. 3 May 2017 161 6 a PET b 7 SUV c 8 SUV a PET b SUV c SUV a b 2 2 f σ H 2 H = ( f xx f xy f xz f yx f yy f yz f zx f zy f zz ) 2 2 3 λ 1 λ 2 λ 3 Z dot Z dot (λ 1, λ 2, λ 3 )={ λ 3 2 / λ 1, λ 1 < 0, λ 2 < 0, λ 3 < 0 0, otherwise 3 PET Z dot 6 4 PET SUV I (x, y, z)=i(x, y, z)+e mass (Z dot (x, y, z)) 4 4 I PET SUV I SUV PET SUV Z dot E mass SUV E mass 7 M 1 M 2 E 1 E 2 PET SUV 8 8 b c SUV 3 SUV PET Th d 9 SUV 4 CT PET 9 c d 5
162 Med Imag Tech Vol. 35 No. 3 May 2017 V FP x y z L B (L B L A ) 0.3 L B x y z Z B1 3. 9 a PET b c d 1 10 10 11 31 42 3 True Point Biograph 40 CT 0.97 0.97 2.00 mm 3 PET 4.07 4.07 2.00 mm 3 10 25.2±9.07 mm SUV 4.44±3.07 Th bone = 150 [H.U.] Th breast = 500 [H.U.] V FP 19 pixel SUV 20 mm SUVmax 1.5 σ H = 1.5 M 1 = 10 7 M 2 = 1.5 10 7 E 1 = 1.0 E 2 = 1.5 20 mm 20 mm SUV Visual studio 2012 2 Th d 1.0 3.0 0.5 1 FROC 3 FROC 10 10 c 1 3.9 76 11 12 13 11 12 13
Med Imag Tech Vol. 35 No. 3 May 2017 163 11 a PET/CT b 12 a PET/CT b 13 a PET/CT b c 10 FROC a b c 4. 10 10 10 c 11 SUVmax 7.56 12 13 1 3.9 76 11
164 Med Imag Tech Vol. 35 No. 3 May 2017 10 mm SUV 1.5 12 V FP 5. PET CT 2 26108005 1 2015 2016, p14 2 66: 484-490, 2003 3 CAD 23: 19-26, 2006 4 Yoshikawa R, Teramoto A, Matsubara T, et al.: Automated detection of architectural distortion using improved adaptive Gabor filter. In Fujita H, Hara T, Muramatsu C, eds., Breast Imaging, Lecture Notes in Computer Science vol. 8539. The 12th International Workshop on Breast Imaging, Springer, Cham, 2014, pp606-611 5 Guan H, Kubota T, Huang X, et al.: Automatic hot spot detection and segmentation in whole body FDG- PET images. In proceedings of the 13th IEEE International Conference in Image Processing, Atlanta, 2006, pp85-88 6 Ballangan C, Wang X, Fulham S, et al.: Lung tumor segmentation in PET images using graph cuts. Comput Methods Programs Biomed 109: 260-268, 2013 7 Teramoto A, Fujita H, Takahashi K, et al.: Hybrid method for the detection of puimonary nodules using positron emission tomography/computed tomography. Int J CARS 9: 59-69, 2014 8 Teramoto A, Fujita H, Takahashi K, et al.: Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positives reduction using a convolutional neural network technique. Med Phys 43: 2821-2827, 2016 9 : CT Med Imag Tech 26: 217-224, 2008 10 : 3 X CT Med Imag Tech 31: 62-71, 2013 11 Teramoto A, Fujita H: Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. Int J CARS 8: 192-205, 2013 12 CT 22: 220-228, 2005 13 CT Med Imag Tech 30: 43-52, 2012 14 Keys JW: SUV: Standard uptake or silly useless value? J Nucl Med 36: 1836-1839, 1995 15 Li Q, Sone S, Doi K, et al.: Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys 30: 2040-2051, 2003
Med Imag Tech Vol. 35 No. 3 May 2017 165 Preliminary Study on an Automated Extraction of Breast Region and Automated Detection of Breast Tumors and Axillary Metastasis Using PET/CT Images Natsuki MINOURA 1, Atsushi TERAMOTO 1, Katsuaki TAKAHASHI 2, Osamu YAMAMURO 2, Masami NISHIO 3, Tsuneo TAMAKI 2, Hiroshi FUJITA 4 1 Graduate School of Health Sciences, Fujita Health University 2 East Nagoya Imaging Diagnosis Center 3 Nagoya Radiological Diagnosis Center 4 Graduate School of Medicine, Gifu University Positron emission tomography PET and X-ray computed tomography CT are used for the localization and analysis of breast cancer and axillary metastasis. In this study, we develop a method for the automated detection of breast tumors and axillary metastasis in PET/CT images. Our scheme extracts the breast region, which includes axilla, from CT images and then detects high-uptake regions inside the breast region from PET images. First, a bounding box is calculated for the breast and the axilla using bone and lung information obtained from CT images. Second, high-uptake regions are detected in PET images using massive structure enhancement and thresholding. The areas outside the breast regions are excluded from initial candidate regions. False positives FPs are eliminated using the location and the shape of initial candidate regions before obtaining final candidate regions. In our experiments, we evaluated tumor detection ability of the proposed method. Breast regions were identified and extracted correctly in all cases. Sensitivity of tumor detection was 0.76 with a number of FPs/case of 3.9. These results indicate that the proposed method may be useful for breast tumor and axillary metastasis detection using PET/CT images. Key words: PET/CT, Breast cancer, Automated detection, Region extraction, Hessian matrix Med Imag Tech 35 3 : 158 166, 2017
166 Med Imag Tech Vol. 35 No. 3 May 2017 2016 PET CAD 1996 1998 4 2005 2007 2008 2012 X 1999 2008 2009 2010 X CT 1991 1999 2017 1991 1993 2000 A 1993 2002 PET 1984 1986 PET CT 1978 1986 1983 1986 1991 1995 2002 CAD 2017 IEEE SPIE * * *