A4 Article in conference proceedings
Low-Dose Mouse Micro-CT Image Segmentation Based on Multi-Resolution Multi-Organ Shape Prior Knowledge Model (2022)

Chen, Z., Wang, H., Cong, F., & Kettunen, L. (2022). Low-Dose Mouse Micro-CT Image Segmentation Based on Multi-Resolution Multi-Organ Shape Prior Knowledge Model. In CIPAE 2022 : 2022 International Conference on Computers, Information Processing and Advanced Education (pp. 349-353). IEEE. https://doi.org/10.1109/cipae55637.2022.00079

JYU authors or editors

Publication details

All authors or editors: Chen, Zhonghua; Wang, Hongkai; Cong, Fengyu; Kettunen, Lauri

Parent publication: CIPAE 2022 : 2022 International Conference on Computers, Information Processing and Advanced Education

Conference:

Place and date of conference: Ottawa, ON, Canada, 26.-28.8.2022

ISBN: 978-1-6654-6813-8

eISBN: 978-1-6654-6812-1

Publication year: 2022

Publication date: 10/02/2023

Pages range: 349-353

Number of pages in the book: 492

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/cipae55637.2022.00079

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/93417

Abstract

Automatic segmentation of computed tomography (CT) images of mice is a step toward computer-assisted preclinical image analysis. Due to the low image quality of micro-CT images, fully-automatic methods may not achieve robust segmentation. For this reason, human interventions are needed to achieve higher segmentation accuracy. In this paper, we propose a human interactive segmentation method incorporating anatomical prior knowledge for multiple abdominal organs in mouse micro-CT images. The method automatically fits a multi-organ shape model to the user-sketched partial boundary contours. Segmentation accuracy is validated by comparing the proposed method against existing shape models. The robustness of our proposed method was evaluated with different users. Finally, the results suggest the proposed method generates accurate segmentation with good robustness.

Keywords: imaging; computed tomography; Three-dimensional imaging; anatomy; lungs; segmentation; animal disease models

Free keywords: image segmentation; image resolution; shape; computed tomography; computational modeling; lung; information processing

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 1