A 4D Statistical Shape Model for Automated Segmentation of Lungs with Large Tumors

Abstract

Abstract. Segmentation of lungs with large tumors is a challenging and time-consuming task, especially for 4D CT data sets used in radiation therapy. Existing lung segmentation methods are ineffective in these cases, because they are either not able to deal with large tumors and/or process every 3D image independently neglecting temporal information. In this paper, we present a approach for model-based 4D segmentation of lungs with large tumors in 4D CT data sets. In our approach, a 4D statistical shape model that accounts for inter-and intra-patient variability is fitted to the 4D image sequence, and the segmentation result is refined by a 4D graph-based optimal surface finding. The approach is evaluated using 10 4D CT data sets of lung tumor patients. The segmentation results are compared with a standard intensity-based approach and a 3D version of the presented model-based segmentation method. The intensity-based approach shows a better performance for normal lungs, however, fails in presence of large lung tumors. Although overall performance of 3D and 4D model-based segmentation is similar, the results indicate improved temporal coherence and improved robustness with respect to the segmentation parameters for the 4D model-based segmentation.

Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach

“…Sofka et al [4] detect the carina of trachea and use a hierarchical detection network to predict pose parameters of left and right lung. Wilms et al [3] use heuristics based on the bronchial tree, Sun et al [1] detect the rib-cage, and Gill et al [2] predict the location of the carina and lung apex to initialize an ASM. The drawbacks of these methods are that, firstly, they depend on the quality of salient point detection.…”

Robust Selection-Based Sparse Shape Model for Lung Cancer Image Segmentation

“…Segmentation methods that only rely on bottom-up information may not be sufficient to handle images exhibiting large inhomogeneities like lung cancer. Supervised learning [11], multi-reference level set [4], hierarchical partial matching [17], and various shape prior models [7,3,8,16,14] were proposed to address these challenges and achieved excellent performance. Recently, Zhang et al [19] modeled shape prior using a sparse shape composition, which produces accurate organ-level segmentation in lung and liver images.…”

Self-learning shape recognition in medical images

“…In [85], a general framework of multi-organ segmentation is proposed that incorporates interrelations among multiple organs and models conditional shape and local priors without the prior intensity knowledge. Besides organs, SSM as well as its variations have proven to achieve fairly good results in small region recognition [88,89]. In [89], 4D-SSM is derived for lungs with large tumors segmentation in 4D CT data.…”