Automatic detection of anatomical landmarks of the aorta in CTA images.
Computed tomography angiography (CTA) is one of the most common vascular imaging modalities. However, for clinical use, it still requires laborious manual analysis. This study demonstrates the feasibility of a fully automated technology for the accurate detection and identification of several anatom...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 5; pp. 903 - 920 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2020
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| 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=142943682&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142943682 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2020 vid: 58 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142943682 142943682 NLM32072432 10.1007/s11517-019-02110-x NLM32072432 142943682 ppf: 903 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic detection of anatomical landmarks of the aorta in CTA images. aug: au: Tahoces, Pablo G. Santana-Cedrés, Daniel Alvarez, Luis Alemán-Flores, Miguel Trujillo, Agustín Cuenca, Carmelo Carreira, Jose M. affil: Department Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, Spain sug: ab: Computed tomography angiography (CTA) is one of the most common vascular imaging modalities. However, for clinical use, it still requires laborious manual analysis. This study demonstrates the feasibility of a fully automated technology for the accurate detection and identification of several anatomical reference points (landmarks), commonly used in intravascular imaging. This technology uses two different approaches, specially designed for the detection of aortic root and supra-aortic and visceral branches. In order to adjust the parameters of the developed algorithms, a total of 33 computed tomography scans with different types of pathologies were selected. Furthermore, a total of 30 independently selected computed tomography scans were used to assess their performance. Accuracy was evaluated by comparing the locations of reference points manually marked by human experts with those that were automatically detected. For supra-aortic and visceral branches detection, average values of 91.8 % for recall and 98.8 % for precision were obtained. For aortic root detection, the average difference between the positions marked by the experts and those detected by the computer was 5.7 ± 7.3 mm. Finally, diameters and lengths of the aorta were measured at different locations related to the extracted landmarks. Those measurements agreed with the values reported by the literature. Graphical abstract Schematic description of the proposed algorithm. The input includes an already segmented aorta (left), there are two main sub-processes related to the detection of branches and roots (center), and the output includes the segmented original aorta with the branches and the detected landmarks superimposed (right). pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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