Titlebook: Deformable Meshes for Medical Image Segmentation; Accurate Automatic S Dagmar Kainmueller Book 2015 Springer Fachmedien Wiesbaden 2015 Auto

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2625-9354 ta is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author’s core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art D

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Book 2015urate fully automatic segmentation of anatomical structures in 3D medical image data. The author’s core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-sh

損壞

From Surface Mesh Deformations to Volume Deformationsexhibit large non-linear . variations of shape that are of . nature rather than stemming from a statistical distribution. E.g., articulated bone compounds are subject to intra-individual shape variation stemming from articulation in joints. Such variations call for mechanical rather than statistical modeling (Kainmueller et al., 2009b).

損壞

Fundamentals of Quantitative Evaluations more accurate than method B. As for the quantification of segmentation accuracy, Section 6.1 introduces a set of measures that quantify the dissimilarity of one segmentation to another. These measures quantify differences between segmentations.

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https://doi.org/10.1007/978-3-642-68912-3the methodological and practical context in which this thesis was realized, lists the specific contributions of this work, and distinguishes related topics that are not discussed. Section 1.3 gives an overview of the structure of this thesis.

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