Automatic prostate segmentation with boundary-enhanced Unet-TIC model(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第6期
- Page:
- 719-725
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Automatic prostate segmentation with boundary-enhanced Unet-TIC model
- Author(s):
- CHEN Hongtao; ZHENG Fang; GAO Yan; SHI Yabin; DENG Xiaonian; ZHONG Heli
- Department of Radiation Oncology, Shenzhen Peoples Hospital (the Second Clinical Medical College, Jinan University the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China
- Keywords:
- Keywords: Unet Unet-TIC coherence-enhancing diffusion boundary enhancement automatic segmentation Dice similarity coefficient
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.06.011
- Abstract:
- Objective To improve the accuracy of automatic prostate segmentation with image boundary enhancement method and optimized Unet model. Methods The prostate scans of 50 patients in Shenzhen Peoples Hospital and 50 cases from MICCAI Grand Challenge database were analyzed in this study, with 81 cases for training set, 10 cases for validation set, 9 cases for test set. The coherence-enhancing diffusion filtering with optimized rotation invariance (CED-ORI) was used to enhance the image boundary. Unet-two input channel (Unet-TIC) was established, with two input contraction paths for the parallel capture of the features from original and CED-ORI images, sharing one expansion path, and it could highlight features contributed to CED-ORI boundary enhancement through concatenation layers, thereby capturing more multi-level information for enhancing upsampling resolution. The performances of Unet, Unet-c and Unet-TIC were evaluated by accuracy, mean DSC, median DSC, ASD, MSD and RVD. Results Both Unet-c and Unet-TIC had better segmentation performances than Unet, and Unet-TIC which had the best performance was superior to Unet in all 6 evaluation indexes. Compared with those of Unet, the accuracy, mean DSC and median DSC of Unet-TIC were improved by 1.87%, 1.81% and 1.21%, respectively, and ASD, MSD and RVD were decreased by 0.32 mm, 1.63 mm and 4.64%, respectively. Moreover, Unet-TIC was more accurate in visual delineation than Unet, and it was able to capture complex shape changes of the prostate, especially identifying confusing and similar boundary areas. Conclusion Unet-TIC is advantageous over Unet in organs segmentation and delineation.
Last Update: 2022-06-27