ArticleMedical physics2021
Automated segmentation of lung, liver, and liver tumors from Tc-99m MAA SPECT/CT images for Y-90 radioembolization using convolutional neural networks.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Artificial intelligence and oral microbiome: Reshaping the diagnostic and therapeutic paradigm of OSCC.Clinical and translational medicine · 2026Review
- Revisiting the segmentation threshold for Lu-177 SPECT.Medical physics · 2026Article
- Effects of a deep learning-based image quality enhancement method on a digital-BGO PET/CT system forEJNMMI physics · 2025Article
- A paired multi-scale attention network for liver tumor segmentation in 99mTc-MAA SPECT/CT imaging.Scientific reports · 2025Article
- Machine-learning based quantification of lung shunt fraction from 99mTc-MAA SPECT/CT for selective internal radiation therapy of liver tumors using TriDFusion (3DF).EJNMMI physics · 2025Article
- Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.Japanese journal of radiology · 2025Review
- Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies.Scientific reports · 2024Article
- New frontiers in radioembolization.Therapeutic advances in medical oncology · 2024Review
- Technological Advancements in Interventional Oncology.Diagnostics (Basel, Switzerland) · 2023Review
- Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis.Frontiers in oncology · 2023Article
- Automated segmentation of lung, liver, and liver tumors from Tc-99m MAA SPECT/CT images for Y-90 radioembolization using convolutional neural networks.Medical physics · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
purpose
methods
resultsThe evaluation results show that the method can accurately segment lungs, liver, and tumor with median [interquartile range] DSCs of 0.98 [0.97-0.98], 0.91 [0.83-0.93], and 0.85 [0.71-0.88]; surface DSCs of 0.99 [0.97-0.99], 0.86 [0.77-0.93], and 0.85 [0.62-0.93], and ASSDs of 0.91 [0.69-1.5], 4.8 [2.6-8.4], and 4.7 [3.5-9.2] mm, respectively. Dosimetric parameters from the three segmentation networks show relationship with those from the reference segmentations. The overall segmentation took about 1 min per patient on an NVIDIA RTX-2080Ti GPU.
conclusionThis work presents CNN-based algorithms to segment lungs, liver, and tumor from
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