Abstract：
:An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Tong T,Wolz R,Wang Z,Gao Q,Misawa K,Fujiwara M,Mori K,Hajnal JV,Rueckert Ddoi
10.1016/j.media.2015.04.015subject
Has Abstractpub_date
2015-07-01 00:00:00pages
92-104issue
1eissn
1361-8415issn
1361-8423pii
S1361-8415(15)00065-1journal_volume
23pub_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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