ArticleIEEE transactions on bio-medical engineering2014
Segmentation of PET images for computer-aided functional quantification of tuberculosis in small animal models.
Article in IEEE transactions on bio-medical engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 64 citations in OpenAlex.
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- DeepPulmoTB: A benchmark dataset for multi-task learning of tuberculosis lesions in lung computerized tomography (CT).Heliyon · 2024Article
- A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images.iScience · 2023Article
- Does consensus contours improve robustness and accuracy onEJNMMI physics · 2023Article
- Clinical Feasibility Study of Gold Nanoparticles as Theragnostic Agents for Precision Radiotherapy.Biomedicines · 2022Article
- Implications of the Harmonization of [Tomography (Ann Arbor, Mich.) · 2022Article
- Lung Cancer Detection and Improving Accuracy Using Linear Subspace Image Classification Algorithm.Interdisciplinary sciences, computational life sciences · 2021Article
- Automated detection of third molars and mandibular nerve by deep learning.Scientific reports · 2019Article
- Quantitative Analysis of Heterogeneous [Molecular imaging and biology · 2019Article
- A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation.IEEE transactions on bio-medical engineering · 2019Article
- Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2019Article
- Accurate, robust and harmonized implementation of morpho-functional imaging in treatment planning for personalized radiotherapy.PloS one · 2019Article
- A semi-automatic technique to quantify complex tuberculous lung lesions onEJNMMI research · 2018Article
- Accurate segmenting of cervical tumors in PET imaging based on similarity between adjacent slices.Computers in biology and medicine · 2018Article
- Joint solution for PET image segmentation, denoising, and partial volume correction.Medical image analysis · 2018Article
- Primary lung tumor segmentation from PET-CT volumes with spatial-topological constraint.International journal of computer assisted radiology and surgery · 2016Article
- Lower Respiratory Tract Infection of the Ferret by 2009 H1N1 Pandemic Influenza A Virus Triggers Biphasic, Systemic, and Local Recruitment of Neutrophils.Journal of virology · 2015Article
- A review on segmentation of positron emission tomography images.Computers in biology and medicine · 2014Review
- Segmentation based denoising of PET images: an iterative approach via regional means and affinity propagation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2014Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Pulmonary infections often cause spatially diffuse and multi-focal radiotracer uptake in positron emission tomography (PET) images, which makes accurate quantification of the disease extent challenging. Image segmentation plays a vital role in quantifying uptake due to the distributed nature of immuno-pathology and associated metabolic activities in pulmonary infection, specifically tuberculosis (TB). For this task, thresholding-based segmentation methods may be better suited over other methods; however, performance of the thresholding-based methods depend on the selection of thresholding parameters, which are often suboptimal. Several optimal thresholding techniques have been proposed in the literature, but there is currently no consensus on how to determine the optimal threshold for precise identification of spatially diffuse and multi-focal radiotracer uptake. In this study, we propose a method to select optimal thresholding levels by utilizing a novel intensity affinity metric within the affinity propagation clustering framework. We tested the proposed method against 70 longitudinal PET images of rabbits infected with TB. The overall dice similarity coefficient between the segmentation from the proposed method and two expert segmentations was found to be 91.25 ±8.01% with a sensitivity of 88.80 ±12.59% and a specificity of 96.01 ±9.20%. High accuracy and heightened efficiency of our proposed method, as compared to other PET image segmentation methods, were reported with various quantification metrics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.