Multi-class medical image segmentation using one-vs-rest graph cuts and majority voting Journal Article
| Authors: | Hu, Y. C.; Mageras, G.; Grossberg, M. |
| Article Title: | Multi-class medical image segmentation using one-vs-rest graph cuts and majority voting |
| Abstract: | Purpose: Semi-automatic image segmentation is still a valuable tool in clinical applications since it retains the expert oversights legally required. However, semi-automatic methods for simultaneous multi-class segmentation are difficult to be clinically implemented due to the complexity of underlining algorithms. We purpose an efficient one-vs-rest graph cut approach of which the complexity only grows linearly as the number of classes increases. Approach: Given an image slice, we construct multiple one-vs-rest graphs, each for a tissue class, for inference of a conditional random field (CRF). The one-vs-rest graph cut is to minimize the CRF energy derived from regional and boundary class probabilities estimated from random forests to obtain a one-vs-rest segmentation. The final segmentation is obtained by fusing from those one-vs-rest segmentations based on majority voting. We compare our method to a well-used multi-class graph cut method, alpha-beta swap, and a fully connected CRF (FCCRF) method, in brain tumor segmentation of 20 high-grade tumor cases in 2013 MICCAI dataset. Results: Our method achieved mean Dice score of 0.83 for whole tumor, compared to 0.80 by alpha-beta swap and 0.79 by FCCRF. There was a performance improvement over alpha-beta swap by a factor of five. Conclusions: Our method utilizes the probabilistic-based CRF which can be estimated from any machine learning technique. Comparing to traditional multi-class graph cut, the purposed one-vs-rest approach has complexity that grows only linearly as the number of classes increases, therefore, our method can be applicable for both online semi-automatic and offline automatic segmentation in clinical applications. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE). |
| Keywords: | automation; tumors; decision trees; clinical application; image segmentation; semi-automatic segmentation; graphic methods; automatic segmentations; learning systems; random processes; graph cuts; machine learning techniques; medical image processing; conditional random field; brain tumor segmentation; multi-class segmentation; computational complexity; multi-class segmentations; semi-automatic image segmentation; semiautomatic methods |
| Journal Title: | Journal of Medical Imaging |
| Volume: | 8 |
| Issue: | 3 |
| ISSN: | 2329-4302 |
| Publisher: | SPIE |
| Date Published: | 2021-05-01 |
| Start Page: | 034003 |
| Language: | English |
| DOI: | 10.1117/1.Jmi.8.3.034003 |
| PROVIDER: | scopus |
| PMCID: | PMC8223166 |
| PUBMED: | 34179219 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 2 August 2021 -- Source: Scopus |
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