ArticleInternational journal of computer assisted radiology and surgery2019
Dual-modality multi-atlas segmentation of torso organs from [
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 14 citations in OpenAlex.
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- Quantitative imaging biomarkers of immune-related adverse events in immune-checkpoint blockade-treated metastatic melanoma patients: a pilot study.European journal of nuclear medicine and molecular imaging · 2022Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
purposeAutomated segmentation of torso organs from positron emission tomography/computed tomography (PET/CT) images is a prerequisite step for nuclear medicine image analysis. However, accurate organ segmentation from clinical PET/CT is challenging due to the poor soft tissue contrast in the low-dose CT image and the low spatial resolution of the PET image. To overcome these challenges, we developed a multi-atlas segmentation (MAS) framework for torso organ segmentation from 2-deoxy-2-[
methodOur key idea is to use PET information to compensate for the imperfect CT contrast and use surface-based atlas fusion to overcome the low PET resolution. First, all the organs are segmented from CT using a conventional MAS method, and then the abdomen region of the PET image is automatically cropped. Focusing on the cropped PET image, a refined MAS segmentation of the abdominal organs is performed, using a surface-based atlas fusion approach to reach subvoxel accuracy.
resultsThis method was validated based on 69 PET/CT images. The Dice coefficients of the target organs were between 0.80 and 0.96, and the average surface distances were between 1.58 and 2.44 mm. Compared to the CT-based segmentation, the PET-based segmentation gained a Dice increase of 0.06 and an ASD decrease of 0.38 mm. The surface-based atlas fusion leads to significant accuracy improvement for the liver and kidneys and saved ~ 10 min computation time compared to volumetric atlas fusion.
conclusionsThe presented method achieves better segmentation accuracy than conventional MAS method within acceptable computation time for clinical applications.
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