Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching

Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of 0.9773±0.0254, which was statistically significantly better (p value ≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes

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

Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation

Comparative Study With New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy

[EN] The impact of positron emission tomography (PET) on radiation therapy is held back by poor methods of defining functional volumes of interest. Many new software tools are being proposed for contouring target volumes but the different approaches are not adequately compared and their accuracy is poorly evaluated due to the ill-definition of ground truth. This paper compares the largest cohort to date of established, emerging and proposed PET contouring methods, in terms of accuracy and variability. We emphasize spatial accuracy and present a new metric that addresses the lack of unique ground truth. Thirty methods are used at 13 different institutions to contour functional volumes of interest in clinical PET/CT and a custom-built PET phantom representing typical problems in image guided radiotherapy. Contouring methods are grouped according to algorithmic type, level of interactivity and how they exploit structural information in hybrid images. Experiments reveal benefits of high levels of user interaction, as well as simultaneous visualization of CT images and PET gradients to guide interactive procedures. Method-wise evaluation identifies the danger of over-automation and the value of prior knowledge built into an algorithm.For retrospective patient data and manual ground truth delineation, the authors wish to thank S. Suilamo, K. Lehtio, M. Mokka, and H. Minn at the Department of Oncology and Radiotherapy, Turku University Hospital, Finland. This study was funded by the Finnish Cancer Organisations.Shepherd, T.; Teräs, M.; Beichel, RR.; Boellaard, R.; Bruynooghe, M.; Dicken, V.; Gooding, MJ.... (2012). Comparative Study With New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy. IEEE Transactions on Medical Imaging. 31(12):2006-2024. doi:10.1109/TMI.2012.2202322S20062024311

Reconstruction and Representation of Tubular Structures using Simplex Meshes

Modelling and reconstruction of tubular objects is a known problem in computer graphics. For computer aided surgical planning the constructed geometrical models need to be consistent and compact at the same time, which known approaches cannot guarantee. In this paper we present a new method for generating compact, topologically consistent, 2-manifold surfaces of branching tubular objects using a two-stage approach. The proposed method is based on connection of polygonal cross-sections along the medial axis and subsequent re nement. Higher order furcations can be handled correctly

Constructing Smooth Non-Manifold Meshes of Multi-Labeled Volumetric Datasets

This paper presents a method for constructing consistent non-manifold meshes of multi-labeled volu- metric datasets. This approach is di erent to traditional surface reconstruction algorithms which often only support extracting 2-manifold surfaces based on a binary voxel classi cation. However, in some { especially medical { applications, multi-labeled datasets, where up to eight di erently labeled voxels can be adjacent, are subject to visualization resulting in non-manifold meshes. In addition to an e cient surface reconstruction method, a constrained geometric lter is developed which can be applied to these non-manifold meshes without producing ridges at mesh junctions

A Novel Robust Tube Detection Filter for 3D Centerline Extraction

Abstract. Centerline extraction of tubular structures such as blood vessels and airways in 3D volume data is of vital interest for applications involving registration, segmentation and surgical planing. In this paper, we propose a robust method for 3D centerline extraction of tubular structures. The method is based on a novel multiscale medialness function and additionally provides an accurate estimate of tubular radius. In contrast to other approaches, the method does not need any user selected thresholds and provides a high degree of robustness. For comparison and performance evaluation, we are using both synthetic images from a public database and a liver CT data set. Results show the advantages of the proposed method compared with the methods of Frangi et al. and Krissian et al.

Constructing smooth nonmanifold meshes of multi-labeled volumetric datasets

This paper presents a method for constructing consistent non-manifold meshes of multi-labeled volumetric datasets. This approach is different to traditional surface reconstruction algorithms which often only support extracting 2-manifold surfaces based on a binary voxel classification. However, in some – especially medical – applications, multi-labeled datasets, where up to eight differently labeled voxels can be adjacent, are subject to visualization resulting in non-manifold meshes. In addition to an efficient surface reconstruction method, a constrained geometric filter is developed which can be applied to these non-manifold meshes without producing ridges at mesh junctions