Segmentation precision of abdominal anatomy for MRI-based radiotherapy

被引:9
|
作者
Noel, Camille E. [1 ]
Zhu, Fan [1 ]
Lee, Andrew Y. [1 ]
Hu, Yanle [1 ]
Parikh, Parag J. [1 ]
机构
[1] Washington Univ, Sch Med, Dept Radiat Oncol, St Louis, MO 63110 USA
关键词
Intraobserver interobserver contouring; precision; Abdomen; Magnetic resonance imaging; Treatment planning; IMAGE SEGMENTATION; DELINEATION; VALIDATION; CANCER;
D O I
10.1016/j.meddos.2014.02.003
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
The limited soft tissue visualization provided by computed tomography, the standard imaging modality for radiotherapy treatment planning and daily localization, has motivated studies on the use of magnetic resonance imaging (MRI) for better characterization of treatment sites, such as the prostate and head and neck. However, no studies have been conducted on MRI-based segmentation for the abdomen, a site that could greatly benefit from enhanced soft tissue targeting. We investigated the interobserver and intraobserver precision in segmentation of abdominal organs on MR images for treatment planning and localization. Manual segmentation of 8 abdominal organs was performed by 3 independent observers on MR images acquired from 14 healthy subjects. Observers repeated segmentation 4 separate times for each image set. Interobserver and intraobserver contouring precision was assessed by computing 3-dimensional overlap (Dice coefficient [DC]) and distance to agreement (Hausdorff distance [HD]) of segmented organs. The mean and standard deviation of intraobserver and interobserver DC and HD values were DCintraobserver = 0.89 +/- 0.12, HDintraobserver = 3.6 mm +/- 1.5, DCinterobserver = 0.89 +/- 0.15, and HDinterobserver = 3.2 mm +/- 1.4. Overall, metrics indicated good interobserver/intraobserver precision (mean DC > 0.7, mean HD < 4 mm). Results suggest that MRI offers good segmentation precision for abdominal sites. These findings support the utility of MRI for abdominal planning and localization, as emerging MRI technologies, techniques, and onboard imaging devices are beginning to enable MET-based radiotherapy. (C) 2014 American Association of Medical Dosimetrists.
引用
收藏
页码:212 / 217
页数:6
相关论文
共 50 条
  • [41] MRI-based adaptive radiotherapy has the potential to reduce dysphagia in patients with head and neck cancer
    Grepl, Jakub
    Sirak, Igor
    Vosmik, Milan
    Pohankova, Denisa
    Hodek, Miroslav
    Paluska, Petr
    Tichy, Ales
    PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2023, 105
  • [42] MRI-based tumor inter-fraction motion statistics for rectal cancer boost radiotherapy
    Kleijnen, Jean-Paul J. E.
    van Asselen, Bram
    Van den Begin, Robbe
    Intven, Martijn
    Burbach, Johannes P. M.
    Reerink, Onne
    Philippens, Marielle E. P.
    de Ridder, Mark
    Lagendijk, Jan J. W.
    Raaymakers, Bas W.
    ACTA ONCOLOGICA, 2019, 58 (02) : 232 - 236
  • [43] Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and Classification
    Hussain, Syed Sajid
    Wani, Niyaz Ahmad
    Kaur, Jasleen
    Ahmad, Naveed
    Ahmad, Sadique
    IEEE ACCESS, 2025, 13 : 41141 - 41158
  • [44] Multiatlas segmentation of thoracic and abdominal anatomy with level set-based local search
    Schreibmann, Eduard
    Marcus, David M.
    Fox, Tim
    JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, 2014, 15 (04): : 22 - 38
  • [45] MRI-based methods for perfusion imaging
    Jedrzejewski, Grzegorz
    POLISH JOURNAL OF RADIOLOGY, 2006, 71 (04) : 52 - 54
  • [46] MRI-Based Lung Tumor Tracking with Navigator Echo Pulses
    Mooney, K.
    Mistry, N.
    Diwanji, T.
    Lin, J.
    Shi, X.
    Regine, W.
    D'Souza, W.
    MEDICAL PHYSICS, 2014, 41 (06) : 524 - 524
  • [47] Abdominal Adiposity Quantification at MRI via Fuzzy Model-Based Anatomy Recognition
    Tong, Yubing
    Udupa, J. K.
    Odhner, D.
    Sin, Sanghun
    Arens, R.
    MEDICAL IMAGING 2013: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING, 2013, 8672
  • [48] MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases
    Li, Ruikun
    Guo, Yujie
    Zhao, Zhongchen
    Chen, Mingming
    Liu, Xiaoqing
    Gong, Guanzhong
    Wang, Lisheng
    EUROPEAN RADIOLOGY, 2023, 33 (05) : 3521 - 3531
  • [49] MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases
    Ruikun Li
    Yujie Guo
    Zhongchen Zhao
    Mingming Chen
    Xiaoqing Liu
    Guanzhong Gong
    Lisheng Wang
    European Radiology, 2023, 33 : 3521 - 3531
  • [50] Cervical spine osteoradionecrosis or bone metastasis after radiotherapy for nasopharyngeal carcinoma? The MRI-based radiomics for characterization
    Zhong, Xi
    Li, Li
    Jiang, Huali
    Yin, Jinxue
    Lu, Bingui
    Han, Wen
    Li, Jiansheng
    Zhang, Jian
    BMC MEDICAL IMAGING, 2020, 20 (01)