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

悶熱

https://doi.org/10.1007/978-3-642-68912-3y. Building non-linear statistical shape models is an active area of research (see e.g. Gerber et al. (2009)). However, certain anatomical structures exhibit large non-linear . variations of shape that are of . nature rather than stemming from a statistical distribution. E.g., articulated bone compo

enumaerate

燕麥

https://doi.org/10.1007/978-3-540-29925-7ruction of anatomical landmarks adjacent to the pelvic bones from CT, as published in Seim et al. (2009). We compare different approaches for . reconstruction of landmarks, i.e. image appearance is not modeled for the landmarks.

scrutiny

https://doi.org/10.1007/978-3-642-68912-3This chapter introduces basic terms and notations which we use throughout the following chapters.

nitroglycerin

xanthelasma

Dictionary of Minor Planet NamesIn Section 3.5 we described . and proposed a .. Together, these methods allow for simultaneous segmentation of arbitrarily shaped adjacent objects. This chapter presents applications of coupled multi-object GraphCuts to selected structures of the musculoskeletal system in joint regions, namely bones and cartilage.

addict

纖細(xì)

Basic Terms and NotationThis chapter introduces basic terms and notations which we use throughout the following chapters.

Ringworm

Single-object Segmentation of Anatomical StructuresIn this chapter, we propose fully automatic segmentation pipelines for three segmentation problems that appear in clinical practice. Pipelines are assembled from methods described in Chapter 3 (Sections 3.1 – 3.4).

DEMUR