ArticleMedical image analysis2018
The first MICCAI challenge on PET tumor segmentation.
Article in Medical image analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 2 of them syntheses that pooled it.
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60 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature.Diagnostics (Basel, Switzerland) · 2023Review
- Multimodal Pediatric Lymphoma Detection using PET and MRI.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- From coarse to fine: a deep 3D probability volume contours framework for tumour segmentation and dose painting in PET images.Frontiers in radiology · 2023Article
- Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT.Head and neck tumor segmentation and outcome prediction : third challenge, HECKTOR 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Head and Neck Tumor Segmentation Challenge (3rd : 2022 : Singapor... · 2023Article
- Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning.Frontiers in veterinary science · 2023Article
- An EANM position paper on the application of artificial intelligence in nuclear medicine.European journal of nuclear medicine and molecular imaging · 2022Article
- Detection and localization of hyperfunctioning parathyroid glands on [Radiology and oncology · 2022Article
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Authors and funding
21 authors.
Funding
Abstract
introductionAutomatic functional volume segmentation in PET images is a challenge that has been addressed using a large array of methods. A major limitation for the field has been the lack of a benchmark dataset that would allow direct comparison of the results in the various publications. In the present work, we describe a comparison of recent methods on a large dataset following recommendations by the American Association of Physicists in Medicine (AAPM) task group (TG) 211, which was carried out within a MICCAI (Medical Image Computing and Computer Assisted Intervention) challenge. MATERIALS AND
methodsOrganization and funding was provided by France Life Imaging (FLI). A dataset of 176 images combining simulated, phantom and clinical images was assembled. A website allowed the participants to register and download training data (n = 19). Challengers then submitted encapsulated pipelines on an online platform that autonomously ran the algorithms on the testing data (n = 157) and evaluated the results. The methods were ranked according to the arithmetic mean of sensitivity and positive predictive value.
resultsSixteen teams registered but only four provided manuscripts and pipeline(s) for a total of 10 methods. In addition, results using two thresholds and the Fuzzy Locally Adaptive Bayesian (FLAB) were generated. All competing methods except one performed with median accuracy above 0.8. The method with the highest score was the convolutional neural network-based segmentation, which significantly outperformed 9 out of 12 of the other methods, but not the improved K-Means, Gaussian Model Mixture and Fuzzy C-Means methods.
conclusionThe most rigorous comparative study of PET segmentation algorithms to date was carried out using a dataset that is the largest used in such studies so far. The hierarchy amongst the methods in terms of accuracy did not depend strongly on the subset of datasets or the metrics (or combination of metrics). All the methods submitted by the challengers except one demonstrated good performance with median accuracy scores above 0.8.
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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.