Detection of Lung Contour with Closed Principal Curve and Machine Learning.
Radiation therapy plays an essential role in the treatment of cancer. In radiation therapy, the ideal radiation doses are delivered to the observed tumor while not affecting neighboring normal tissues. In three-dimensional computed tomography (3D-CT) scans, the contours of tumors and organs-at-risk...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 4; pp. 520 - 534 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2018
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131471434&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131471434 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2018 vid: 31 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131471434 131471434 131471434 10.1007/s10278-018-0058-y 131471434 ppf: 520 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Detection of Lung Contour with Closed Principal Curve and Machine Learning. aug: au: Peng, Tao Wang, Yihuai Xu, Thomas Canhao Shi, Lianmin Jiang, Jianwu Zhu, Shilang affil: School of Computer Science & Technology, Soochow University, No.1 Shizi Road, 215006, Suzhou, Jiangsu, China sug: subj: Lung Neoplasms Radiotherapy Machine Learning Tomography, X-Ray Computed Imaging, Three-Dimensional Lung Radiation Dosage Algorithms Radiotherapy Adverse Effects Validity Descriptive Statistics ab: Radiation therapy plays an essential role in the treatment of cancer. In radiation therapy, the ideal radiation doses are delivered to the observed tumor while not affecting neighboring normal tissues. In three-dimensional computed tomography (3D-CT) scans, the contours of tumors and organs-at-risk (OARs) are often manually delineated by radiologists. The task is complicated and time-consuming, and the manually delineated results will be variable from different radiologists. We propose a semi-supervised contour detection algorithm, which firstly uses a few points of region of interest (ROI) as an approximate initialization. Then the data sequences are achieved by the closed polygonal line (CPL) algorithm, where the data sequences consist of the ordered projection indexes and the corresponding initial points. Finally, the smooth lung contour can be obtained, when the data sequences are trained by the backpropagation neural network model (BNNM). We use the private clinical dataset and the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset to measure the accuracy of the presented method, respectively. To the private dataset, experimental results on the initial points which are as low as 15% of the manually delineated points show that the Dice coefficient reaches up to 0.95 and the global error is as low as 1.47 × 10−2. The performance of the proposed algorithm is also better than the cubic spline interpolation (CSI) algorithm. While on the public LIDC-IDRI dataset, our method achieves superior segmentation performance with average Dice of 0.83. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|