Learning longitudinal deformations for adaptive segmentation of lung fields from serial chest radiographs

Yonghong Shi, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We previously developed a deformable model for segmenting lung fields in serial chest radiographs by using both population-based and patient-specific shape statistics, and obtained higher accuracy compared to other methods. However, this method uses an ad hoc way to evenly partition the boundary of lung fields into some short segments, in order to capture the patient-specific shape statistics from a small number of samples by principal component analysis (PCA). This ad hoc partition can lead to a segment including points with different amounts of longitudinal deformations, thus rendering it difficult to capture principal variations from a small number of samples using PCA. In this paper, we propose a learning technique to adaptively partition the boundary of lung fields into short segments according to the longitudinal deformations learned for each boundary point. Therefore, all points in the same short segment own similar longitudinal deformations and thus small variations within all longitudinal samples of a patient, which enables effective capture of patient-specific shape statistics by PCA. Experimental results show the improved performance of the proposed method in segmenting the lung fields from serial chest radiographs.

Original languageEnglish
Title of host publicationMedical Imaging and Augmented Reality - 4th International Workshop, Proceedings
Pages413-420
Number of pages8
DOIs
Publication statusPublished - 2008
Event4th International Workshop on Medical Imaging and Augmented Reality, MIAR 2008 - Tokyo, Japan
Duration: 2008 Aug 12008 Aug 2

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5128 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Workshop on Medical Imaging and Augmented Reality, MIAR 2008
CountryJapan
CityTokyo
Period08/8/108/8/2

Keywords

  • Active shape model
  • Hierarchical principal component analysis
  • Scale space analysis
  • Statistical model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Shi, Y., & Shen, D. (2008). Learning longitudinal deformations for adaptive segmentation of lung fields from serial chest radiographs. In Medical Imaging and Augmented Reality - 4th International Workshop, Proceedings (pp. 413-420). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5128 LNCS). https://doi.org/10.1007/978-3-540-79982-5_45