A4 Artikkeli konferenssijulkaisussa
Multi-resolution Statistical Shape Models for Multi-organ Shape Modelling (2020)

Chen, Z., Ristaniemi, T., Cong, F., & Wang, H. (2020). Multi-resolution Statistical Shape Models for Multi-organ Shape Modelling. In M. Han, S. Qin, & N. Zhang (Eds.), ISNN 2020 : Advances in Neural Networks : 17th International Symposium on Neural Networks, Proceedings (pp. 74-84). Springer. Lecture Notes in Computer Science, 12557. https://doi.org/10.1007/978-3-030-64221-1_7

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Chen, Zhonghua; Ristaniemi, Tapani; Cong, Fengyu; Wang, Hongkai

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

Emojulkaisun toimittajat: Han, Min; Qin, Sitian; Zhang, Nian

Konferenssin paikka ja aika: Cairo, Egypt, 4.-6.12.2020

ISBN: 978-3-030-64220-4

eISBN: 978-3-030-64221-1

Lehti tai sarja: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Julkaisuvuosi: 2020

Sarjan numero: 12557

Artikkelin sivunumerot: 74-84

Kustantaja: Springer

Kustannuspaikka: Cham

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/93664

Tiivistelmä

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.

YSO-asiasanat: tietokonetomografia; sisäelimet; 3D-mallinnus; tilastolliset mallit

Vapaat asiasanat: multi-resolution multi-organ SSM; PCA; HDLSS; mouse micro-CT image

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 1