ArticlePhysics in medicine and biology2020
A physics-guided modular deep-learning based automated framework for tumor segmentation in PET.
Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- CTLESS: A scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.IEEE transactions on medical imaging · 2024Article
- Deep Semisupervised Transfer Learning for Fully Automated Whole-Body Tumor Quantification and Prognosis of Cancer on PET/CT.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- 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
- Improving Generalizability of PET DL Algorithms: List-Mode Reconstructions Improve DOTATATE PET Hepatic Lesion Detection Performance.Bioengineering (Basel, Switzerland) · 2024Article
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- Observer-study-based approaches to quantitatively evaluate the realism of synthetic medical images.Physics in medicine and biology · 2023Article
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- Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning.Current medical imaging · 2023Review
- 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
- Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET.EJNMMI research · 2022Article
- 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
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Authors and funding
7 authors.
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Abstract
An important need exists for reliable positron emission tomography (PET) tumor-segmentation methods for tasks such as PET-based radiation-therapy planning and reliable quantification of volumetric and radiomic features. To address this need, we propose an automated physics-guided deep-learning-based three-module framework to segment PET images on a per-slice basis. The framework is designed to help address the challenges of limited spatial resolution and lack of clinical training data with known ground-truth tumor boundaries in PET. The first module generates PET images containing highly realistic tumors with known ground-truth using a new stochastic and physics-based approach, addressing lack of training data. The second module trains a modified U-net using these images, helping it learn the tumor-segmentation task. The third module fine-tunes this network using a small-sized clinical dataset with radiologist-defined delineations as surrogate ground-truth, helping the framework learn features potentially missed in simulated tumors. The framework was evaluated in the context of segmenting primary tumors in
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