Discriminative dictionary learning for abdominal multi-organ segmentation.

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 Anal

journal_title

Medical image analysis

authors

Tong T,Wolz R,Wang Z,Gao Q,Misawa K,Fujiwara M,Mori K,Hajnal JV,Rueckert D

doi

10.1016/j.media.2015.04.015

subject

Has Abstract

pub_date

2015-07-01 00:00:00

pages

92-104

issue

1

eissn

1361-8415

issn

1361-8423

pii

S1361-8415(15)00065-1

journal_volume

23

pub_type

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