Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends.
The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routine...
| Publicado en: | RadioGraphics Vol. 35; no. 4; pp. 1056 - 1077 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | review Journal Article |
| Publicado: |
Wolters Kluwer Health
2015 Jul-Aug
|
| 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=109598142&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109598142 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02715333 4ZQC jtl: RadioGraphics issn: 02715333 maglogo: N pubinfo: dt: 2015 Jul-Aug vid: 35 iid: 4 pid: 79357 pub: Wolters Kluwer Health place: New York, New York artinfo: ui: 109598142 109598142 NLM26172351 2013095157 10.1148/rg.2015140232 NLM26172351 PMC4521615 109598142 ppf: 1056 ppct: 21 formats: tig: atl: Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends. aug: au: Mansoor, Awais Bagci, Ulas Foster, Brent Xu, Ziyue Papadakis, Georgios Z Folio, Les R Udupa, Jayaram K Mollura, Daniel J sug: ab: The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|