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International Journal of Computer Assisted Radiology and Surgery

Published in:

01-05-2017 | Original Article

Fast approximation for joint optimization of segmentation, shape, and location priors, and its application in gallbladder segmentation

Authors: Atsushi Saito, Shigeru Nawano, Akinobu Shimizu

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 5/2017

Abstract

Purpose

This paper addresses joint optimization for segmentation and shape priors, including translation, to overcome inter-subject variability in the location of an organ. Because a simple extension of the previous exact optimization method is too computationally complex, we propose a fast approximation for optimization. The effectiveness of the proposed approximation is validated in the context of gallbladder segmentation from a non-contrast computed tomography (CT) volume.

Methods

After spatial standardization and estimation of the posterior probability of the target organ, simultaneous optimization of the segmentation, shape, and location priors is performed using a branch-and-bound method. Fast approximation is achieved by combining sampling in the eigenshape space to reduce the number of shape priors and an efficient computational technique for evaluating the lower bound.

Results

Performance was evaluated using threefold cross-validation of 27 CT volumes. Optimization in terms of translation of the shape prior significantly improved segmentation performance. The proposed method achieved a result of 0.623 on the Jaccard index in gallbladder segmentation, which is comparable to that of state-of-the-art methods. The computational efficiency of the algorithm is confirmed to be good enough to allow execution on a personal computer.

Conclusions

Joint optimization of the segmentation, shape, and location priors was proposed, and it proved to be effective in gallbladder segmentation with high computational efficiency.

Available only for authorised users

It should be noted that optimization in an eigenshape space has a great advantage over the method of Lempitsky [20] in terms of computational cost. Interested readers should refer to the paper [21] for more details.

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Title: Fast approximation for joint optimization of segmentation, shape, and location priors, and its application in gallbladder segmentation
Authors: Atsushi Saito
Shigeru Nawano
Akinobu Shimizu
Publication date: 01-05-2017
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 5/2017
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1571-z

At a glance: The STEP trials

Springer Medicine

Fast approximation for joint optimization of segmentation, shape, and location priors, and its application in gallbladder segmentation

Abstract

Purpose

Methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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