Abstract：
:In this paper, we propose a novel automated pipeline for extraction of sulcal fundi from triangulated cortical surfaces. This method consists of four consecutive steps. Firstly, we adopt a finite difference method to estimate principal curvatures, principal directions and curvature derivatives, along the principal directions, for each vertex. Then, we detect the sulcal fundi segment in each triangle of the cortical surface based on curvatures and curvature derivatives. Afterwards, we link the sulcal fundi segments into continuous curves. Finally, we connect breaking sulcal fundi and smooth bumping sulcal fundi by using the fast marching method on the cortical surface. The proposed method can find the accurate sulcal fundi using curvatures and curvature derivatives without any manual interaction. The method was applied to 10 normal brain MR images on inner cortical surfaces. We quantitatively evaluated the accuracy of the sulcal fundi extraction method using manually labeled sulcal fundi by experts. The average difference between automatically extracted major sulcal fundi and the expert labeled results is consistently around 1.0mm on 10 subject images, indicating the good performance of the proposed method.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Li G,Guo L,Nie J,Liu Tdoi
10.1016/j.media.2010.01.005subject
Has Abstractpub_date
2010-06-01 00:00:00pages
343-59issue
3eissn
1361-8415issn
1361-8423pii
S1361-8415(10)00014-9journal_volume
14pub_type
杂志文章abstract：:Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model proper...
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journal_title：Medical image analysis
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doi：10.1016/j.media.2013.07.005
更新日期：2013-12-01 00:00:00
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