A4 Article in conference proceedings
Multi-resolution Statistical Shape Models for Multi-organ Shape Modelling (2020)


Chen, Zhonghua; Ristaniemi, Tapani; Cong, Fengyu; Wang, Hongkai (2020). Multi-resolution Statistical Shape Models for Multi-organ Shape Modelling. In Han, Min; Qin, Sitian; Zhang, Nian (Eds.) ISNN 2020 : Advances in Neural Networks : 17th International Symposium on Neural Networks, Proceedings (pp. 74-84). Lecture Notes in Computer Science, 12557. Cham: Springer. DOI: 10.1007/978-3-030-64221-1_7


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Publication details

All authors or editors: Chen, Zhonghua; Ristaniemi, Tapani; Cong, Fengyu; Wang, Hongkai

Parent publication: ISNN 2020 : Advances in Neural Networks : 17th International Symposium on Neural Networks, Proceedings

Parent publication editors: Han, Min; Qin, Sitian; Zhang, Nian

Place and date of conference: Cairo, Egypt, 4.-6.12.2020

ISBN: 978-3-030-64220-4

eISBN: 978-3-030-64221-1

Journal or series: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Publication year: 2020

Number in series: 12557

Pages range: 74-84

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-030-64221-1_7

Open Access: Publication channel is not openly available


Abstract

Statistical shape models (SSMs) are widely used in medical image segmentation. However, traditional SSM methods suffer from the High-Dimension-Low-Sample-Size (HDLSS) problem in modelling. In this work, we extend the state-of-the-art multi-resolution SSM approach from two dimension (2D) to three dimension (3D) and from single organ to multiple organs. Then we proposed a multi-resolution multi-organ 3D SSM method that uses a downsampling-and-interpolation strategy to overcome HDLSS problem. We also use an inter-surface-point distance thresholding scheme to achieve multi-resolution modelling effect. Our method is tested on the modelling of multiple mouse abdominal organs from mouse micro-CT images in three different resolution levels, including global level, single organ level and local organ level. The minimum specificity error and generalization error of this method are less than 0.3 mm, which are close to the pixel resolution of mouse micro-CT images (0.2 mm) and better than the modelling results of traditional principal component analysis (PCA) method.


Keywords: computed tomography; internal organs; Three-dimensional imaging; statistical models

Free keywords: multi-resolution multi-organ SSM; PCA; HDLSS; mouse micro-CT image


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Preliminary JUFO rating: 1


Last updated on 2020-02-12 at 10:56