Abstract
The precise extraction of the contour of prostate on transrectal ultrasound (TRUS) is crucial for the diagnosis and treatment of prostate tumor. Due to the relatively low signal-to-noise ratio (SNR) of TRUS images and the potential of imaging artifacts, accurate contouring of the prostate from TRUS images has been a challenging task. This paper proposes four strategies to achieve higher precision of segmentation on TRUS images. Firstly, a modified principal curve-based algorithm is used to obtain the data sequence, with a small amount of prior point information adopted for coarse initialization. Secondly, an evolution neural network is devised to find an optimal network. Thirdly, a fractional-order-based network is trained with the data sequence as input, resulting in a decreased model error and increased precision. Finally, the parameters of a fractional-order-based neural network were utilized to construct an interpretable and smooth mathematical equation of the organ border. The Dice similarity coefficient (DSC), Jaccard similarity coefficient (OMG), and accuracy (ACC) of model outputs against ground-truths were 95.9 ± 2.3%, 94.9 ± 2.4%, and 95.3 ± 2.2%, respectively. The results of our method outperform several popular state-of-the-art segmentation methods.
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References
Zong, J., Qiu, T., Li, W., Guo, D.: Automatic ultrasound image segmentation based on local entropy and active contour model. Comput. Math. Appl. 78, 929–943 (2019)
Panigrahi, L., Verma, K., Singh, B.K.: Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution. Expert Syst. Appl. 115, 486–498 (2019)
Huang, K., Zhang, Y., Cheng, H.D., Xing, P., Zhang, B.: Semantic segmentation of breast ultrasound image with fuzzy deep learning network and breast anatomy constraints. Neurocomputing. 450, 319–335 (2021)
Jaouen, V., et al.: Prostate volume segmentation in TRUS using hybrid edge-Bhattacharyya active surfaces. IEEE Trans. Biomed. Eng. 66, 920–933 (2018)
Lu, X., et al.: Ultrasonographic pathological grading of prostate cancer using automatic region-based Gleason grading network. Comput. Med. Imaging Graph., 102125 (2022)
Beitone, C., Troccaz, J.: Multi-eXpert fusion: an ensemble learning framework to segment 3D TRUS prostate images. Med. Phys. 49, 5138–5148 (2022)
van Sloun, R.J.G., et al.: Deep learning for real-time, automatic, and scanner-adapted prostate (Zone) segmentation of transrectal ultrasound, for example, magnetic resonance imaging–transrectal ultrasound fusion prostate biopsy, European urology. Focus. 7, 78–85 (2021)
Guo, Y., Şengür, A., Akbulut, Y., Shipley, A.: An effective color image segmentation approach using neutrosophic adaptive mean shift clustering. Measurement 119, 28–40 (2018)
Wu, R., Wang, B., Xu, A.: Functional data clustering using principal curve methods. Commun. Stat., 1–20 (2021)
Ge, Y., et al.: Distributed differential evolution based on adaptive mergence and split for large-scale optimization. IEEE Trans. Cybern. 48, 2166–2180 (2018)
Chen, M.-R., Chen, B.-P., Zeng, G.-Q., Lu, K.-D., Chu, P.: An adaptive fractional-order BP neural network based on extremal optimization for handwritten digits recognition. Neurocomputing. 391, 260–272 (2020)
Biau, G., Fischer, A.: Parameter selection for principal curves. IEEE Trans. Inf. Theory 58, 1924–1939 (2012)
Wang, Y., et al.: Deep attentive features for prostate segmentation in 3D transrectal ultrasound. IEEE Trans. Med. Imaging 38, 2768–2778 (2019)
Moraes, E.C.C., Ferreira, D.D., Vitor, G.B., Barbosa, B.H.G.: Data clustering based on principal curves. Adv. Data Anal. Classif. 14, 77–96 (2020)
Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17, 790–799 (1995)
Guo, Y., Şengür, A.: A novel image segmentation algorithm based on neutrosophic similarity clustering. Appl. Soft Comput. 25, 391–398 (2014)
Hastie, T., Stuetzle, W.: Principal curves. J. Am. Stat. Assoc. 84, 502–516 (1989)
Kégl, B., Linder, T., Zeger, K.: Learning and design of principal curves. IEEE Trans. Pattern Anal. Mach. Intell. 22, 281–297 (2000)
Celebi, M.E., Celiker, F., Kingravi, H.A.: On Euclidean norm approximations. Pattern Recogn. 44, 278–283 (2011)
Zeng, Y.-R., Zeng, Y., Choi, B., Wang, L.: Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Energy 127, 381–396 (2017)
Leema, N., Nehemiah, H.K., Kannan, A.: Neural network classifier optimization using differential evolution with global information and back propagation algorithm for clinical datasets. Appl. Soft Comput. 49, 834–844 (2016)
Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13, 945–958 (2009)
Xiao, M., Zheng, W.X., Jiang, G., Cao, J.: Undamped oscillations generated by Hopf bifurcations in fractional-order recurrent neural networks with Caputo derivative. IEEE Trans. Neural Netw. Learn. Syst. 26, 3201–3214 (2015)
Han, J., Moraga, C.: The influence of the sigmoid function parameters on the speed of backpropagation learning. In: Mira, J., Sandoval, F. (eds.) From Natural to Artificial Neural Computation, pp. 195–201. Springer, Heidelberg (1995)
Hara, K., Saito, D., Shouno, H.: Analysis of function of rectified linear unit used in deep learning. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2015)
Peng, T., Wu, Y., Qin, J., Wu, Q.J., Cai, J.: H-ProSeg: hybrid ultrasound prostate segmentation based on explainability-guided mathematical model. Comput. Methods Programs Biomed. 219, 106752 (2022)
Peng, T., et al.: H-ProMed: ultrasound image segmentation based on the evolutionary neural network and an improved principal curve. Pattern Recogn. 131, 108890 (2022)
Niu, S., Chen, Q., de Sisternes, L., Ji, Z., Zhou, Z., Rubin, D.L.: Robust noise region-based active contour model via local similarity factor for image segmentation. Pattern Recogn. 61, 104–119 (2017)
Liu, Y., He, C., Gao, P., Wu, Y., Ren, Z.: A binary level set variational model with L1 data term for image segmentation. Sig. Process. 155, 193–201 (2019)
Benaichouche, A.N., Oulhadj, H., Siarry, P.: Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction. Digit. Sig. Process. 23, 1390–1400 (2013)
Gao, Y., Zhou, M., Metaxas, D.: UTNet: a hybrid transformer architecture for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 61–71 (2021)
Peng, T., Gu, Y., Ye, Z., Cheng, X., Wang, J.: A-LugSeg: automatic and explainability-guided multi-site lung detection in chest X-ray images. Expert Syst. Appl. 198, 116873 (2022)
Peng, T., Tang, C., Wu, Y., Cai, J.: H-SegMed: a hybrid method for prostate segmentation in TRUS images via improved closed principal curve and improved enhanced machine learning. Int. J. Comput. Vis. 130, 1896–1919 (2022)
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Peng, T. et al. (2023). Delineation of Prostate Boundary from Medical Images via a Mathematical Formula-Based Hybrid Algorithm. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14261. Springer, Cham. https://doi.org/10.1007/978-3-031-44198-1_14
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