In:
Journal of Medical Imaging and Health Informatics, American Scientific Publishers, Vol. 10, No. 11 ( 2020-11-01), p. 2681-2685
Kurzfassung:
A deep learning based active contour framework is proposed for pancreas segmentation. Data extension and fractional differential operation are firstly applied for pre-processing. Second, deep learning method is designed to acquire the initial contour of pancreas. Subsequently, an intensity
constrained term is designed to stop the contours at the edges. The intensity constrained term is integrated into a variational active contour model with three terms. The accurate pancreas segmentation is obtained by the evolution of the active contour model. Our approach reaches high detection dice similarity coefficient (DSC) of 83% and sensitivity of 85% in a dataset containing 40 abdominal CT scans. Comparisons with other level set models provide evidence that the proposed method offers desirable performances.
Materialart:
Online-Ressource
ISSN:
2156-7018
DOI:
10.1166/jmihi.2020.32002681
Sprache:
Englisch
Verlag:
American Scientific Publishers
Publikationsdatum:
2020
Permalink
