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Docteur de l'université Automatic Segmentation and Shape Analysis ...

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18 Chapter 2 Literature Review<br />

Depending on the application <strong>and</strong> imaging modalities used in acquisition, a wi<strong>de</strong><br />

range of segmentation methods has been <strong>de</strong>veloped in or<strong>de</strong>r to <strong>de</strong>al with images<br />

of different parts of body. With respect to the level of human involvement, these<br />

methods can be classified into three categories:<br />

• manual segmentation<br />

• semi-automatic segmentation<br />

• automatic segmentation.<br />

Manual segmentation is usually performed by trained experts following certain<br />

protocols <strong>de</strong>fining regions of interest (ROIs) on the image. The anatomical knowl-<br />

edge of the expert contributes to the segmentation when the boundaries of the<br />

structures are difficult to <strong>de</strong>tect solely based on image features. Although expert-<br />

<strong>de</strong>lineated segmentations are regar<strong>de</strong>d as the ground truth or the gol<strong>de</strong>n st<strong>and</strong>ard,<br />

in practice, manual approaches are subjected to both intra- <strong>and</strong> inter-rater vari-<br />

ability induced by the the human factor. Consi<strong>de</strong>rable variability may result in<br />

low reproducibility of the manual segmentation. In addition, manually tracing the<br />

structures of interest by experts is a laborious task, which can be time consuming<br />

<strong>and</strong> expensive. It might take more than one hour per structure for the expert to<br />

manually <strong>de</strong>fine the boundary of the structure of interest in the volume, which is<br />

impractical <strong>and</strong> inefficient, especially when large data set is involved. Computer<br />

softwares are usually used to assist manual tracing <strong>and</strong> the edition of the bound-<br />

aries, such as the publicly available MRIcron (Ror<strong>de</strong>n <strong>and</strong> Brett, 2000), imageJ<br />

(Abramoff et al., 2004), 3D Slicer (Pieper et al., 2006) <strong>and</strong> ITK-SNAP (Yushkevich<br />

et al., 2006).<br />

Computerized automatic <strong>and</strong> semi-automatic methods are introduced to reduce<br />

the subjectivity in the manual segmentation, <strong>and</strong> to increase the efficiency <strong>and</strong><br />

reproducibility. Semi-automatic segmentation still requires manual operations to<br />

introduce prior knowledge, provi<strong>de</strong> guidance to the process, or correct the segmen-<br />

tation results. The correction of boundaries sometimes can take as long as manual

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