Chinese Journal of Magnetic Resonance ›› 2021, Vol. 38 ›› Issue (3): 356-366.doi: 10.11938/cjmr20212885
• Articles • Previous Articles Next Articles
Shi-ju YAN1,Yong-sen HAN2,*(),Guang-yu TANG2
Received:
2021-01-26
Online:
2021-09-05
Published:
2021-08-26
Contact:
Yong-sen HAN
E-mail:hanys@163.com
CLC Number:
Shi-ju YAN,Yong-sen HAN,Guang-yu TANG. An Improved Level Set Algorithm for Prostate Region Segmentation[J]. Chinese Journal of Magnetic Resonance, 2021, 38(3): 356-366.
Fig.3
Comparison between the segmentation results with the algorithm proposed in this research and those with ground truth. The upper and lower lines represent two different patients respectively. The left, middle, and right columns represent the slices of the bottom, middle, and top of the prostate, respectively. Green contour is ground truth, while red contour is segmented by our algorithm
1 | WORLD HEALTH ORGANIZATION. Latest global cancer data: Cancer burden rises to 19.3 million new cases and 10.0 million cancer deaths in 2020 questions and answers (Q & A)[OL]. https://www.iarc.fr/faq/latest-global-cancer-data-2020-qa. |
2 |
ANAS E M A , MOUSAVI P , ABOLMAESUMI P . A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy[J]. Med Image Anal, 2018, 48, 107- 116.
doi: 10.1016/j.media.2018.05.010 |
3 | WANG L J , SU X Y , LI Y , et al. Segmentation of right ventricle in cardiac cine MRI using COLLATE fusion-based multi-atlas[J]. Chinese J Magn Reson, 2018, 35 (4): 407- 416. |
王丽嘉, 苏新宇, 李亚, 等. 基于COLLATE融合多图谱的心脏电影MRI右心室分割[J]. 波谱学杂志, 2018, 35 (4): 407- 416. | |
4 |
WANG X L . Application of histogram analysis of dynamic enhanced MRI quantitative parameter in the diagnosis of prostate cancer[J]. Chinese Journal of CT and MRI, 2020, 18 (12): 110- 113.
doi: 10.3969/j.issn.1672-5131.2020.12.035 |
王晓蕾. 动态增强MRI定量参数直方图分析在诊断前列腺癌中的应用[J]. 中国CT和MRI杂志, 2020, 18 (12): 110- 113.
doi: 10.3969/j.issn.1672-5131.2020.12.035 |
|
5 |
PALUMBO P , MANETTA R , IZZO A , et al. Biparametric (bp) and multiparametric (mp) magnetic resonance imaging (MRI) approach to prostate cancer disease: a narrative review of current debate on dynamic contrast enhancement[J]. Gland Surg, 2020, 9 (6): 2235- 2247.
doi: 10.21037/gs-20-547 |
6 | LIU K W , LIU Z L , WANG X Y , et al. Prostate cancer diagnosis based on cascaded convolutional neural networks[J]. Chinese J Magn Reson, 2020, 37 (2): 152- 161. |
刘可文, 刘紫龙, 汪香玉, 等. 基于级联卷积神经网络的前列腺磁共振图像分类[J]. 波谱学杂志, 2020, 37 (2): 152- 161. | |
7 | VAFAIE R, ALIREZAIE J, BABYN P. Fully automated model-based prostate boundary segmentation using markov random field in ultrasound images[C]//Fremantle, WA, Australia: International Conference on Digital Image Computing Techniques and Applications (DICTA), 2012. |
8 | KWAK J T , SANKINENI S , XU S , et al. Correlation of magnetic resonance imaging with digital histopathology in prostate[J]. Int J Comput Ass Rad, 2016, 11 (4): 657- 666. |
9 | QIAN C J , WANG L , GAO Y Z , et al. In vivo MRI based prostate cancer identification with random forests and auto-context model[J]. Comput Med Imag Grap, 2014, 52, 44- 57. |
10 |
KORSAGER A S , FORTUNATI V , FEDDE VDL , et al. The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images[J]. Med Phys, 2015, 42 (4): 1614- 1624.
doi: 10.1118/1.4914379 |
11 |
TIAN Z Q , LIU L Z , ZHANG Z F , et al. Superpixel-based segmentation for 3D prostate MR images[J]. IEEE Trans Med Imag, 2016, 35 (3): 791- 801.
doi: 10.1109/TMI.2015.2496296 |
12 |
LI C M , XU C Y , GUI C F , et al. Distance regularized level set evolution and its application to image segmentation[J]. IEEE T Image Process, 2010, 19 (12): 3243- 3254.
doi: 10.1109/TIP.2010.2069690 |
13 | ZHANG Y D , PENG J C , LIU G , et al. Research on the segmentation method of prostate magnetic resonance image based on level set[J]. Chinese Journal of Scientific Instrument, 2017, 38 (2): 416- 424. |
张永德, 彭景春, 刘罡, 等. 基于水平集的前列腺磁共振图像分割方法研究[J]. 仪器仪表学报, 2017, 38 (2): 416- 424. | |
14 | ZHU Z H , YAN S J , RUAN Y , et al. Segmentation of prostate magnetic resonance images based on an improved distance regularized level set evolution (DRLSE) model[J]. Chinese J Magn Reson, 2020, 37 (4): 447- 455. |
朱泽华, 闫士举, 阮渊, 等. 基于改进DRLSE模型的前列腺磁共振图像分割[J]. 波谱学杂志, 2020, 37 (4): 447- 455. | |
15 |
LI C M , KAO C Y , GORE J C , et al. Minimization of region-scalable fitting energy for image segmentation[J]. IEEE T Image Process, 2008, 17 (10): 1940- 1949.
doi: 10.1109/TIP.2008.2002304 |
16 |
JIANG H Y , FENG R J , GAO X H . Level set based on signed pressure force function and its application in liver image segmentation[J]. Wuhan University Journal of Natural Sciences, 2011, 16 (3): 265- 270.
doi: 10.1007/s11859-011-0748-5 |
17 |
ZHANG K H , SONG H H , ZHAND L . ZHANG. Active contours driven by local image fitting energy[J]. Pattern Recogn, 2010, 43 (4): 1199- 1206.
doi: 10.1016/j.patcog.2009.10.010 |
18 |
KARIMI D , ZENG Q , MATHUR P , et al. Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images[J]. Med Image Anal, 2019, 57, 186- 196.
doi: 10.1016/j.media.2019.07.005 |
19 | MILLETARI F, NAVAB N, AHMADI S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C]//Stanford, CA, USA: 2016 Fourth International Conference on 3D Vision (3DV), 2016. |
20 | OJALA T , PIETIKAINEN M , MAENPAA T . Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE T Pattern Anal, 2002, 7 (24): 971- 987. |
21 | OSHER S , FEDKIW R . Level set methods and dynamic implicit surfaces[M]. New York: Springer-Verlag, 2002. |
22 |
ZHAO H K , CHAN T , MERRIMAN B , et al. A variational level setapproach to multiphase motion[J]. J Comput Phys, 1996, 127 (1): 179- 195.
doi: 10.1006/jcph.1996.0167 |
23 |
OTSU N . A threshold selection method from gray-level histogram[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9 (1): 62- 66.
doi: 10.1109/TSMC.1979.4310076 |
24 |
LI C M , HUANG R , DING Z H , et al. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI[J]. IEEE T Image Process, 2011, 20 (7): 2007- 2016.
doi: 10.1109/TIP.2011.2146190 |
25 | XU C Y, YEZZI A, PRINCE J L. On the relationship between parametric and geometric active contours[C]//Pacific Grove, CA, USA: Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers, 2000. doi: 10.1109/ACSSC.2000.911003. |
26 |
ZHANG K , ZHANG L , SONG H , et al. Active contours with selective local or global segmentation: A new formulation and level set method[J]. Image Vision Comput, 2010, 28 (4): 668- 676.
doi: 10.1016/j.imavis.2009.10.009 |
27 |
CHAN T F , VESE L A . Active contours without edges[J]. IEEE T Image Process, 2001, 10 (2): 266- 277.
doi: 10.1109/83.902291 |
28 |
GEERT L , ROBERT T , WENDY V D V , et al. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge[J]. Med Image Anal, 2014, 18 (2): 359- 373.
doi: 10.1016/j.media.2013.12.002 |
29 |
MAHAPATRA D , BUHMANN J M . Visual saliency-based active learning for prostate magnetic resonance imaging segmentation[J]. J Med Imaging, 2016, 3 (1): 014003.
doi: 10.1117/1.JMI.3.1.014003 |
30 | KARIMI D , SAMEI G , KESCH C , et al. Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models[J]. Int J Comput Ass Rad, 2018, 13 (4): 1- 9. |
[1] | Ying-dan HU,Yue CAI,Xu-xia WANG,Si-jie LIU,Yan KANG,Hao LEI,Fu-chun LIN. Magnetic Resonance Imaging the Brain Structures Involved in Nicotine Susceptibility in Rats [J]. Chinese Journal of Magnetic Resonance, 2021, 38(3): 345-355. |
[2] | Qin-yi SHI,Fang YAN,Yang YANG,Yue-fu CHEN,Xiao-lang LIN,Yuan-jun WANG. Image Segmentation of Tooth and Alveolar Bone with the Level Set Model [J]. Chinese Journal of Magnetic Resonance, 2021, 38(2): 182-193. |
[3] | XIN Hong-tao, WU Guang-yao, WEN Zhi, LEI Hao, LIN Fu-chun. Effects of Antiretroviral Therapy on Brain Gray Matter Volumes in AIDS Patients [J]. Chinese Journal of Magnetic Resonance, 2021, 38(1): 69-79. |
[4] | HE Hong-yan, WEI Shu-feng, WANG Hui-xian, YANG Wen-hui. Matrix Gradient Coil: Current Research Status and Perspectives [J]. Chinese Journal of Magnetic Resonance, 2021, 38(1): 140-153. |
[5] | ZHU Ze-hua, YAN Shi-ju, RUAN Yuan, HAN Bang-min. Segmentation of Prostate Magnetic Resonance Images Based on an Improved Distance Regularized Level Set Evolution (DRLSE) Model [J]. Chinese Journal of Magnetic Resonance, 2020, 37(4): 447-455. |
[6] | HU Ge-li, DENG Ye-hui, WANG Kun, JIANG Tian-zi. A New MRI System Architecture Based on 5G Remote Control and Processing [J]. Chinese Journal of Magnetic Resonance, 2020, 37(4): 490-495. |
[7] | WU Ming-di, FENG Jie, JIA Hui-hui, WU Ji-zhi, ZHANG Xin, CHANG Yan, YANG Xiao-dong, SHENG Mao. MRI-Based Morphological Quantification of Developmental Dysplasia of the Hip in Children [J]. Chinese Journal of Magnetic Resonance, 2020, 37(4): 434-446. |
[8] | LIAO Zhi-wen, CHEN Jun-fei, YANG Chun-sheng, ZHANG Zhi, CHEN Li, XIAO Li-zhi, CHEN Fang, LIU Chao-yang. A Design Scheme for 1H/31P Dual-Nuclear Parallel MRI Coil [J]. Chinese Journal of Magnetic Resonance, 2020, 37(3): 273-282. |
[9] | ZHOU You, YANG Yang, SONG Li-qiang, BI Tian-tian, WANG Yue, ZHAO Ying. Effects of Panax quinquefolius L.-Acorus Tatarinowii on Cognitive Deficits and Brain Morphology of Type 1 Diabetic Rats [J]. Chinese Journal of Magnetic Resonance, 2020, 37(3): 332-348. |
[10] | LOU Yun-zhong, LIU Ying, JIANG Hua, ZHANG Hao-wei. A Deep Learning Algorithm for Classifying Meningioma and Auditory Neuroma in the Cerebellopontine Angle from Magnetic Resonance Images [J]. Chinese Journal of Magnetic Resonance, 2020, 37(3): 300-310. |
[11] | ZHAO Shang-yi, WANG Yuan-jun. Classification of Alzheimer's Disease Patients Based on Magnetic Resonance Images and an Improved UNet++ Model [J]. Chinese Journal of Magnetic Resonance, 2020, 37(3): 321-331. |
[12] | XU Peng-cheng, XIAO Liang. A Design Scheme for Data Transmission Module on Multi-Channel Magnetic Resonance Imaging Spectrometers [J]. Chinese Journal of Magnetic Resonance, 2020, 37(3): 283-290. |
[13] | XIAO Liang, LOU Yu-kun, ZHOU Hang-yu. A U-Net Network-Based Rapid Construction of Knee Models for Specific Absorption Rate Estimation [J]. Chinese Journal of Magnetic Resonance, 2020, 37(2): 144-151. |
[14] | LIU Ke-wen, LIU Zi-long, WANG Xiang-yu, CHEN Li, LI Zhao, WU Guang-yao, LIU Chao-yang. Prostate Cancer Diagnosis Based on Cascaded Convolutional Neural Networks [J]. Chinese Journal of Magnetic Resonance, 2020, 37(2): 152-161. |
[15] | WANG Qiang, WEI Shu-feng, WANG Zheng, YANG Wen-hui. Design of Matrix Gradient Coils with Particle Swarm Optimization and the Genetic Algorithm [J]. Chinese Journal of Magnetic Resonance, 2019, 36(4): 463-471. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||