22 research outputs found
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
Graph-Based Airway Tree Reconstruction From Chest CT Scans: Evaluation of Different Features on Five Cohorts
A method for avoiding overlap of left and right lungs in shape model guided segmentation of lungs in CT volumes
Proceedings of the Sixth International Workshop on Pulmonary Image Analysis
International audienc
FDG PET based prediction of response in head and neck cancer treatment: Assessment of new quantitative imaging features.
Introduction18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is now a standard diagnostic imaging test performed in patients with head and neck cancer for staging, re-staging, radiotherapy planning, and outcome assessment. Currently, quantitative analysis of FDG PET scans is limited to simple metrics like maximum standardized uptake value, metabolic tumor volume, or total lesion glycolysis, which have limited predictive value. The goal of this work was to assess the predictive potential of new (i.e., nonstandard) quantitative imaging features on head and neck cancer outcome.MethodsThis retrospective study analyzed fifty-eight pre- and post-treatment FDG PET scans of patients with head and neck squamous cell cancer to calculate five standard and seventeen new features at baseline and post-treatment. Cox survival regression was used to assess the predictive potential of each quantitative imaging feature on disease-free survival.ResultsAnalysis showed that the post-treatment change of the average tracer uptake in the rim background region immediately adjacent to the tumor normalized by uptake in the liver represents a novel PET feature that is associated with disease-free survival (HR 1.95; 95% CI 1.27, 2.99) and has good discriminative performance (c index 0.791).ConclusionThe reported findings define a promising new direction for quantitative imaging biomarker research in head and neck squamous cell cancer and highlight the potential role of new radiomics features in oncology decision making as part of precision medicine