Abstract：
:We present a machine learning approach called shape regression machine (SRM) for efficient segmentation of an anatomic structure that exhibits a deformable shape in a medical image, e.g., left ventricle endocardial wall in an echocardiogram. The SRM achieves efficient segmentation via statistical learning of the interrelations among shape, appearance, and anatomy, which are exemplified by an annotated database. The SRM is a two-stage approach. In the first stage that estimates a rigid shape to solve an automatic initialization problem, it derives a regression solution to object detection that needs just one scan in principle and a sparse set of scans in practice, avoiding the exhaustive scanning required by the state-of-the-art classification-based detection approach while yielding comparable detection accuracy. In the second stage that estimates the nonrigid shape, it again learns a nonlinear regressor to directly associate nonrigid shape with image appearance. The underpinning of both stages is a novel image-based boosting ridge regression (IBRR) method that enables multivariate, nonlinear modeling and accommodates fast evaluation. We demonstrate the efficiency and effectiveness of the SRM using experiments on segmenting the left ventricle endocardium from a B-mode echocardiogram of apical four chamber view. The proposed algorithm is able to automatically detect and accurately segment the LV endocardial border in about 120ms.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Zhou SKdoi
10.1016/j.media.2010.04.002subject
Has Abstractpub_date
2010-08-01 00:00:00pages
563-81issue
4eissn
1361-8415issn
1361-8423pii
S1361-8415(10)00038-1journal_volume
14pub_type
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