The first MICCAI challenge on PET tumor segmentation Journal Article
| Authors: | Hatt, M.; Laurent, B.; Ouahabi, A.; Fayad, H.; Tan, S.; Li, L.; Lu, W.; Jaouen, V.; Tauber, C.; Czakon, J.; Drapejkowski, F.; Dyrka, W.; Camarasu-Pop, S.; Cervenansky, F.; Girard, P.; Glatard, T.; Kain, M.; Yao, Y.; Barillot, C.; Kirov, A.; Visvikis, D. |
| Article Title: | The first MICCAI challenge on PET tumor segmentation |
| Abstract: | Introduction Automatic 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 methods Organization 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. Results Sixteen 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. Conclusion The 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. © 2017 |
| Keywords: | controlled study; comparative study; positron emission tomography; follow up; clinical practice; publication; false negative result; medical imaging; gaussian mixture model; false positive result; positive predictive values; predictive value; qualitative analysis; image segmentation; comparative studies; machine learning; neural networks; ant colony optimization; pipeline; human; priority journal; article; segmentation algorithms; random forest; volume segmentation; convolutional neural network; malignant neoplasm; adaptive region growing; miccai challenge; pet functional volumes; pipelines; fuzzy c means method; medical image computing; arithmetic; dictionary model; fuzzy locally adaptive bayesian; gradient aided region based active contour; spatial distance weighted fuzzy c means; supervised machine learning |
| Journal Title: | Medical Image Analysis |
| Volume: | 44 |
| ISSN: | 1361-8415 |
| Publisher: | Elsevier Science, Inc. |
| Date Published: | 2018-02-01 |
| Start Page: | 177 |
| End Page: | 195 |
| Language: | English |
| DOI: | 10.1016/j.media.2017.12.007 |
| PROVIDER: | scopus |
| PUBMED: | 29268169 |
| PMCID: | PMC7460722 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 2 January 2018 -- Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
Related MSK Work