Patch-Based Label Fusion for Automatic Multi-Atlas-Based Prostate Segmentation in MR Images

被引：2
|
作者：
Yang, Xiaofeng [1 ,2 ]
Jani, Ashesh B. [1 ,2 ]
Rossi, Peter J. [1 ,2 ]
Mao, Hui [2 ,3 ]
Curran, Walter J. [1 ,2 ]
Liu, Tian [1 ,2 ]
机构：
[1] Emory Univ, Dept Radiat Oncol, Atlanta, GA 30322 USA
[2] Emory Univ, Winship Canc Inst, Atlanta, GA 30322 USA
[3] Emory Univ, Dept Radiol & Imaging Sci, Atlanta, GA 30322 USA
来源：
MEDICAL IMAGING 2016: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING | 2016年 / 9786卷
关键词：
Prostate segmentation; MRI; multi-atlas; label fusion; prostate cancer; REGISTRATION; CANCER;
D O I：
10.1117/12.2216424
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
In this paper, we propose a 3D multi-atlas-based prostate segmentation method for MR images, which utilizes patch-based label fusion strategy. The atlases with the most similar appearance are selected to serve as the best subjects in the label fusion. A local patch-based atlas fusion is performed using voxel weighting based on anatomical signature. This segmentation technique was validated with a clinical study of 13 patients and its accuracy was assessed using the physicians' manual segmentations (gold standard). Dice volumetric overlapping was used to quantify the difference between the automatic and manual segmentation. In summary, we have developed a new prostate MR segmentation approach based on nonlocal patch-based label fusion, demonstrated its clinical feasibility, and validated its accuracy with manual segmentations.
引用
收藏
页数：7
相关论文
共 50 条
  • [31] Automatic atlas-based three-label cartilage segmentation from MR knee images
    Shan, Liang
    Zach, Christopher
    Charles, Cecil
    Niethammer, Marc
    MEDICAL IMAGE ANALYSIS, 2014, 18 (07) : 1233 - 1246
  • [32] Optimal number of atlases and label fusion for automatic multi-atlas-based brachial plexus contouring in radiotherapy treatment planning
    Van de Velde, Joris
    Wouters, Johan
    Vercauteren, Tom
    De Gersem, Werner
    Achten, Eric
    De Neve, Wilfried
    Van Hoof, Tom
    RADIATION ONCOLOGY, 2016, 11
  • [33] Patch-based segmentation using refined multifeature for magnetic resonance prostate images
    Huang, Zhe
    Jiang, Shan
    Ding, Yabin
    Cai, Jing
    Liu, Jun
    Yang, Jun
    Yang, Zhiyong
    OPTIK, 2016, 127 (02): : 732 - 737
  • [34] Multi-Atlas-Based Bone Segmentation of the Lower Extremity Using Magnetic Resonance Images
    Jung, Y.
    Simonsen, M. B.
    Petersen, E. T.
    Stilling, M.
    Andersen, M. S.
    MEDICAL PHYSICS, 2024, 51 (10) : 7829 - 7830
  • [35] Discriminative Dimensionality Reduction for Patch-Based Label Fusion
    Sanroma, Gerard
    Benkarim, Oualid M.
    Piella, Gemma
    Wu, Guorong
    Zhu, Xiaofeng
    Shen, Dinggang
    Gonzalez Ballester, Miguel Angel
    MACHINE LEARNING MEETS MEDICAL IMAGING, 2015, 9487 : 94 - 103
  • [36] Atlas Selection for Multi-Atlas-Based Image Segmentation Using Surrogate Modeling
    Zhao, T.
    Ruan, D.
    MEDICAL PHYSICS, 2015, 42 (06) : 3606 - 3606
  • [37] Lung Nodule Detection and Segmentation Using a Patch-Based Multi-Atlas Method
    Alam, Mustafa
    Sankaranarayanan, Ganesh
    Devarajan, Venkat
    2016 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE & COMPUTATIONAL INTELLIGENCE (CSCI), 2016, : 23 - 28
  • [38] Surrogate Function Evaluation and Comparison for Atlas Selection in Multi-Atlas-Based Segmentation
    Xie, Q.
    Ruan, D.
    MEDICAL PHYSICS, 2014, 41 (06) : 102 - 102
  • [39] Automatic segmentation of prostate in MR images using deep learning and multi-atlas techniques
    Moradi, Hamid
    Foruzan, Amir Hossein
    Chen, Yen-Wei
    2018 25TH IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING AND 2018 3RD INTERNATIONAL IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING (ICBME), 2018, : 199 - 202
  • [40] BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in MR Images Using Patch-Based Anatomical Signature
    Yang, X.
    Jani, A.
    Rossi, P.
    Mao, H.
    Curran, W.
    Liu, T.
    MEDICAL PHYSICS, 2016, 43 (06) : 3883 - 3883
12345