Image Segmentation Method for Athlete Knee Joint Injury Using Transformer Model by MIoT

Jiang, Tao; Feng, Feng; Cao, Yu; Yang, Haitao; Raj, Raja Soosaimarian Peter

doi:10.1590/1678-4324-2023230325

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Article - Engineering, Technology and Techniques • Braz. arch. biol. technol. 66 • 2023 • https://doi.org/10.1590/1678-4324-2023230325 copy

Image Segmentation Method for Athlete Knee Joint Injury Using Transformer Model by MIoT

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The segmentation of athlete knee joint injury images can provide doctors with information about the location and extent of the athlete knee joint injury. Therefore, it is significant to segment the images of athlete knee joint injury. However, the traditional image segmentation method of athlete knee injury has the problems of low accuracy of mask region extraction, completion time of extraction and high error rate of segmentation. In the paper, we propose image segmentation method for athlete knee joint injury using the transformer model by the medical Internet of Things (MIoT). First, the MIoT was used as a way to obtain images of knee joint injury of athletes, and the images of knee joint injury of athletes were derived using the MIoT. Second, the exported image is input into the shadow expansion layer of the transformer model, which performs shadow expansion on the athlete knee joint injury image to obtain its mask region, and then the image is input into the patch embedding layer. Finally, after the patch embedding layer extracts the mask patch of the athlete knee joint injury image, the mask patch is input into the transformer block for down-sampling and up-sampling processing, and then the athlete knee joint injury image segmentation result is output using the end backpropagation layer. The results show that the proposed method has a low error rate in extracting the mask region from the knee joint injury image of athletes, and a short completion time for extracting the mask region, the most detailed and comprehensive segmented athlete knee joint injury image, the maximum error rate of image segmentation is only 6.8%, and the maximum value of segmentation time is only 3.96s. It has important research value in the field of athlete knee joint injury diagnosis.

Keywords:
Image segmentation; Knee joint injury; Athletes; Transformer model; Medical Internet of Things; Mask patch.

HIGHLIGHTS

• An image segmentation for athlete knee joint injury using the transformer model is proposed.

• Reducing the complexity of transformer model training.

• The comprehensive performance of this paper's method is verified by several evaluation criteria.

Number of samples	ISFVO [⁶6 Zhang Y, Tian Y. A new image segmentation method based on fractional-varying-order differential. J Beijing Inst Technol (English Edition). 2021; 30(3): 254-264.doi:10.15918/j.jbit1004-0579.2021.028 https://doi.org/10.15918/j.jbit1004-0579... ] method /s	NODIS [⁷7 Basar S, Ali M, Ochoa-Ruiz G, Waheed A, Rodriguez-Hernandez G, Zareei M, A Novel Defocused Image Segmentation Method Based on PCNN and LBP. Ieee Access. 2021;9: 87219-87240. doi: 10.1109/ACCESS.2021.3084905. https://doi.org/10.1109/ACCESS.2021.3084... ] method /s	DLSK [⁸8 Panfilov E, Tiulpin A, Nieminen M, Saarakkala S, Casula V. Deep learning-based segmentation of knee MRI for fully automatic sub-regional morphological assessment of cartilage tissues: data from the osteoarthritis initiative. J Orthop Res. 2022;40(5): 1113-24. doi:10.1002/jor.25150 https://doi.org/0.1002/jor.25150... ] method /s	IAUEIS [⁹9 Shi Y, Zhang J, Ling T, Lu Jiwen, Zheng Y, Yu Q, et al. Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation. Ieee T Med Imaging. 2022; 41(3):608-20. doi: 10.1109/TMI.2021.3117888. https://doi.org/10.1109/TMI.2021.3117888... ] method /s	PWMIS [¹⁰10 Wen Y, Chen L, Deng Y, Zhang Z, Zhou C. Pixel-wise triplet learning for enhancing boundary discrimination in medical image segmentation. Know-based Syst. 2022; 243(11): 108424.doi:10.1016/j.knosys.2022.108424 https://doi.org/10.1016/j.knosys.2022.10... ] method /s	Proposed method/s
500	1.26	1.96	2.64	3.97	2.21	0.78
1000	1.48	2.55	3.84	4.87	2.51	1.22
1500	1.69	3.69	4.58	5.74	2.98	1.64
2000	2.14	4.77	5.99	6.87	3.69	1.87
2500	2.58	5.84	7.84	8.99	3.94	1.92
3000	2.69	6.99	9.61	10.26	4.16	2.01
Average	1.97	4.30	5.75	6.78	3.24	1.57

Methods	Number of samples
Methods	500	1000	1500	2000	2500	3000
Proposed	1.28	1.55	2.47	2.69	3.58	3.96
ISFVO[⁶6 Zhang Y, Tian Y. A new image segmentation method based on fractional-varying-order differential. J Beijing Inst Technol (English Edition). 2021; 30(3): 254-264.doi:10.15918/j.jbit1004-0579.2021.028 https://doi.org/10.15918/j.jbit1004-0579... ]	1.46	1.92	3.48	5.84	6.17	7.25
NODIS[⁷7 Basar S, Ali M, Ochoa-Ruiz G, Waheed A, Rodriguez-Hernandez G, Zareei M, A Novel Defocused Image Segmentation Method Based on PCNN and LBP. Ieee Access. 2021;9: 87219-87240. doi: 10.1109/ACCESS.2021.3084905. https://doi.org/10.1109/ACCESS.2021.3084... ]	1.29	2.01	3.59	4.78	5.93	8.75
DLSK[⁸8 Panfilov E, Tiulpin A, Nieminen M, Saarakkala S, Casula V. Deep learning-based segmentation of knee MRI for fully automatic sub-regional morphological assessment of cartilage tissues: data from the osteoarthritis initiative. J Orthop Res. 2022;40(5): 1113-24. doi:10.1002/jor.25150 https://doi.org/0.1002/jor.25150... ]	1.33	1.84	3.11	5.32	6.35	9.36
IAUEIS[⁹9 Shi Y, Zhang J, Ling T, Lu Jiwen, Zheng Y, Yu Q, et al. Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation. Ieee T Med Imaging. 2022; 41(3):608-20. doi: 10.1109/TMI.2021.3117888. https://doi.org/10.1109/TMI.2021.3117888... ]	1.82	2.16	4.41	5.91	8.69	10.25
PWMIS[¹⁰10 Wen Y, Chen L, Deng Y, Zhang Z, Zhou C. Pixel-wise triplet learning for enhancing boundary discrimination in medical image segmentation. Know-based Syst. 2022; 243(11): 108424.doi:10.1016/j.knosys.2022.108424 https://doi.org/10.1016/j.knosys.2022.10... ]	1.96	3.16	4.87	5.84	6.35	7.63

Methods	Accuracy	PSNR	SSIM	IQI	AUC-ROC
Proposed	0.95	35.12	0.93	0.89	0.92
ISFVO[⁶6 Zhang Y, Tian Y. A new image segmentation method based on fractional-varying-order differential. J Beijing Inst Technol (English Edition). 2021; 30(3): 254-264.doi:10.15918/j.jbit1004-0579.2021.028 https://doi.org/10.15918/j.jbit1004-0579... ]	0.92	32.14	0.89	0.85	0.87
NODIS[⁷7 Basar S, Ali M, Ochoa-Ruiz G, Waheed A, Rodriguez-Hernandez G, Zareei M, A Novel Defocused Image Segmentation Method Based on PCNN and LBP. Ieee Access. 2021;9: 87219-87240. doi: 10.1109/ACCESS.2021.3084905. https://doi.org/10.1109/ACCESS.2021.3084... ]	0.89	30.89	0.87	0.82	0.84
DLSK[⁸8 Panfilov E, Tiulpin A, Nieminen M, Saarakkala S, Casula V. Deep learning-based segmentation of knee MRI for fully automatic sub-regional morphological assessment of cartilage tissues: data from the osteoarthritis initiative. J Orthop Res. 2022;40(5): 1113-24. doi:10.1002/jor.25150 https://doi.org/0.1002/jor.25150... ]	0.91	31.78	0.88	0.84	0.86
IAUEIS[⁹9 Shi Y, Zhang J, Ling T, Lu Jiwen, Zheng Y, Yu Q, et al. Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation. Ieee T Med Imaging. 2022; 41(3):608-20. doi: 10.1109/TMI.2021.3117888. https://doi.org/10.1109/TMI.2021.3117888... ]	0.88	29.72	0.86	0.81	0.82
PWMIS[¹⁰10 Wen Y, Chen L, Deng Y, Zhang Z, Zhou C. Pixel-wise triplet learning for enhancing boundary discrimination in medical image segmentation. Know-based Syst. 2022; 243(11): 108424.doi:10.1016/j.knosys.2022.108424 https://doi.org/10.1016/j.knosys.2022.10... ]	0.90	30.56	0.87	0.83	0.85

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[1] *Correspondence: fengfeng6011@126.com(F.F.).