ArticlePhysics in medicine and biology2021
A Bayesian approach to tissue-fraction estimation for oncological PET segmentation.
Article in Physics in medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Extending Regression Without Truth to Integrate Ground-Truth Measurements for Evaluating Quantitative Imaging Methods with Patient Data.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Comparing evaluation of quantitative imaging methods using reference standards versus regression‑without‑truth‑based technique.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Observer-study-based approaches to quantitatively evaluate the realism of synthetic medical images.Physics in medicine and biology · 2023Article
- Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Carotid atherosclerotic plaque segmentation in multi-weighted MRI using a two-stage neural network: advantages of training with high-resolution imaging and histology.Frontiers in cardiovascular medicine · 2023Article
- Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines).Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- A tissue-fraction estimation-based segmentation method for quantitative dopamine transporter SPECT.Medical physics · 2022Article
- Ideal-Observer Computation with anthropomorphic phantoms using Markov chain Monte Carlo.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022Article
- Investigating the Impact of Voxel Size and Postfiltering on Quantitative Analysis of Positron Emission Tomography/Computed Tomography: A Phantom Study.Journal of medical physicsArticle
- No-gold-standard evaluation of quantitative imaging methods in the presence of correlated noise.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Investigating the limited performance of a deep-learning-based SPECT denoising approach: An observer-study-based characterization.Proceedings of SPIE--the International Society for Optical EngineeringArticle
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6 authors.
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Abstract
Tumor segmentation in oncological PET is challenging, a major reason being the partial-volume effects (PVEs) that arise due to low system resolution and finite voxel size. The latter results in tissue-fraction effects (TFEs), i.e. voxels contain a mixture of tissue classes. Conventional segmentation methods are typically designed to assign each image voxel as belonging to a certain tissue class. Thus, these methods are inherently limited in modeling TFEs. To address the challenge of accounting for PVEs, and in particular, TFEs, we propose a Bayesian approach to tissue-fraction estimation for oncological PET segmentation. Specifically, this Bayesian approach estimates the posterior mean of the fractional volume that the tumor occupies within each image voxel. The proposed method, implemented using a deep-learning-based technique, was first evaluated using clinically realistic 2D simulation studies with known ground truth, in the context of segmenting the primary tumor in PET images of patients with lung cancer. The evaluation studies demonstrated that the method accurately estimated the tumor-fraction areas and significantly outperformed widely used conventional PET segmentation methods, including a U-net-based method, on the task of segmenting the tumor. In addition, the proposed method was relatively insensitive to PVEs and yielded reliable tumor segmentation for different clinical-scanner configurations. The method was then evaluated using clinical images of patients with stage IIB/III non-small cell lung cancer from ACRIN 6668/RTOG 0235 multi-center clinical trial. Here, the results showed that the proposed method significantly outperformed all other considered methods and yielded accurate tumor segmentation on patient images with Dice similarity coefficient (DSC) of 0.82 (95% CI: 0.78, 0.86). In particular, the method accurately segmented relatively small tumors, yielding a high DSC of 0.77 for the smallest segmented cross-section of 1.30 cm
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