Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue

被引:3
作者
Sawyer, Travis W. [1 ]
Rice, Photini F. S. [2 ]
Sawyer, David M. [3 ]
Koevary, Jennifer W. [2 ]
Barton, Jennifer K. [1 ,2 ]
机构
[1] Univ Arizona, Coll Opt Sci, Tucson, AZ 85721 USA
[2] Univ Arizona, Dept Biomed Engn, Tucson, AZ 85721 USA
[3] Tucson Med Ctr, Tucson, AZ USA
来源
DIAGNOSIS AND TREATMENT OF DISEASES IN THE BREAST AND REPRODUCTIVE SYSTEM IV | 2018年 / 10472卷
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
optical coherence tomography; image segmentation; image processing; ovarian cancer; NEURAL-NETWORK; TEXTURE ANALYSIS; DIAGNOSIS; SURVIVAL; CANCER;
D O I
10.1117/12.2283375
中图分类号
R71 [妇产科学];
学科分类号
100211 ;
摘要
Ovarian cancer has the lowest survival rate among all gynecologic cancers due to predominantly late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Optical coherence tomography (OCT) is an emerging technique that provides depth-resolved, high-resolution images of biological tissue in real time and demonstrates great potential for imaging of ovarian tissue. Mouse models are crucial to quantitatively assess the diagnostic potential of OCT for ovarian cancer imaging; however, due to small organ size, the ovaries must first be separated from the image background using the process of segmentation. Manual segmentation is time-intensive, as OCT yields three-dimensional data. Furthermore, speckle noise complicates OCT images, frustrating many processing techniques. While much work has investigated noise-reduction and automated segmentation for retinal OCT imaging, little has considered the application to the ovaries, which exhibit higher variance and inhomogeneity than the retina. To address these challenges, we evaluated a set of algorithms to segment OCT images of mouse ovaries. We examined five preprocessing techniques and six segmentation algorithms. While all pre-processing methods improve segmentation, Gaussian filtering is most effective, showing an improvement of 32% +/- 1.2%. Of the segmentation algorithms, active contours performs best, segmenting with an accuracy of 0.948 +/- 0.012 compared with manual segmentation (1.0 being identical). Nonetheless, further optimization could lead to maximizing the performance for segmenting OCT images of the ovaries.
引用
收藏
页数:13
相关论文
共 50 条
[41]   Stack generalized deep ensemble learning for retinal layer segmentation in Optical Coherence Tomography images [J].
Anoop, B. N. ;
Pavan, Rakesh ;
Girish, G. N. ;
Kothari, Abhishek R. ;
Rajan, Jeny .
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2020, 40 (04) :1343-1358
[42]   Automatic choroidal segmentation in optical coherence tomography images based on curvelet transform and graph theory [J].
Eghtedar, Reza Alizadeh ;
Esmaeili, Mahdad ;
Peyman, Alireza ;
Akhlaghi, Mohammadreza ;
Rasta, Seyed Hossein .
JOURNAL OF MEDICAL SIGNALS & SENSORS, 2023, 13 (02) :92-100
[43]   A Review of Machine Learning Algorithms for Retinal Cyst Segmentation on Optical Coherence Tomography [J].
Wei, Xing ;
Sui, Ruifang .
SENSORS, 2023, 23 (06)
[44]   Automated combination of optical coherence tomography images and fundus images [J].
Fida, A. D. ;
Gaidel, A., V ;
Demin, N. S. ;
Ilyasova, N. Yu ;
Zamytskiy, E. A. .
COMPUTER OPTICS, 2021, 45 (05) :721-+
[45]   Segmentation of Corneal Optical Coherence Tomography Images Using Randomized Hough Transform [J].
Elsawy, Amr ;
Abdel-Mottaleb, Mohamed ;
Abou Shousha, Mohamed .
MEDICAL IMAGING 2019: IMAGE PROCESSING, 2019, 10949
[46]   Automated segmentation of intraretinal layers from macular optical coherence tomography images [J].
Haeker, Mona ;
Sonka, Milan ;
Kardon, Randy ;
Shah, Vinay A. ;
Wu, Xiaodong ;
Abramoff, Michael D. .
MEDICAL IMAGING 2007: IMAGE PROCESSING, PTS 1-3, 2007, 6512
[47]   Automated retinal layer segmentation in optical coherence tomography images with intraretinal fluid [J].
Wang, Luquan ;
Li, Xiaowen ;
Chen, Yong ;
Han, Dingan ;
Wang, Mingyi ;
Zeng, Yaguang ;
Zhong, Junping ;
Wang, Xuehua ;
Ji, Yanhong ;
Xiong, Honglian ;
Wei, Xunbin .
JOURNAL OF INNOVATIVE OPTICAL HEALTH SCIENCES, 2022, 15 (03)
[48]   Texture, analysis of optical coherence tomography images: feasibility for tissue classification [J].
Gossage, KW ;
Tkaczyk, TS ;
Rodriguez, JJ ;
Barton, JK .
JOURNAL OF BIOMEDICAL OPTICS, 2003, 8 (03) :570-575
[49]   Speckle reduction in optical coherence tomography images using tissue viscoelasticity [J].
Kennedy, Brendan F. ;
Curatolo, Andrea ;
Hillman, Timothy R. ;
Saunders, Christobel M. ;
Sampson, David D. .
JOURNAL OF BIOMEDICAL OPTICS, 2011, 16 (02)
[50]   SPARSITY-BASED RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY IMAGES [J].
Tokayer, Jason ;
Ortega, Antonio ;
Huang, David .
2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2011, :449-452