Abstract：Aiming at the problems of boundary blurring and feature loss in kidney tumor image segmentation, a kidney tumor image segmentation method based on uncertainty guidance and scale consistency is proposed on the basis of residual attention U-net model. For blurred boundaries of kidney tumor images, an uncertainty guidance module is introduced into the decoding layer to allocate weights adaptively based on uncertainty. Thus, the effect of wrong pixels is reduced and the boundary localization ability of the model is improved. For the problem of feature loss caused by down-sampling, the scale attention module and feature consistency loss are proposed. The multi-scale fusion strategy is utilized to fuse features of different scales, and the scale consistency constraint is conducted with the features at the bottom of the encoder to alleviate the problem of feature loss. Finally, experiments of kidney and kidney tumor segmentation on the public dataset KiTS19 demonstrate that the proposed segmentation method greatly improves the segmentation accuracy. In addition, the segmentation results of the proposed method hold better reliability due to the uncertainty guidance module.
[1] 孙君顶,惠朕堃,唐朝生,等.基于U-Net的特征交互分割方法.模式识别与人工智能, 2021, 34(11): 1058-1068. (SUN J D, HUI Z K, TANG C S, et al. U-Net Based Feature Interaction Segmentation Method. Pattern Recognition and Artificial Intelligence, 2021, 34(11): 1058-1068.) [2] 高程玲,叶海良,曹飞龙.基于三重交互关注网络的医学图像分割算法.模式识别与人工智能, 2021, 34(5): 398-406. (GAO C L, YE H L, CAO F L.Medical Image Segmentation via Triplet Interactive Attention Network. Pattern Recognition and Artificial Intelligence, 2021, 34(5): 398-406.) [3] RONNEBERGER O, FISCHER P, BROX T.U-Net: Convolutional Networks for Biomedical Image Segmentation // Proc of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin Germany: Springer, 2015: 234-241. [4] ZHOU Z W,
SIDDIQUEE M M R,
TAJBAKHSH N, et al.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation // Proc of the International Workshop on Deep Learning in Medical Image Analysis. Berlin, Germany: Springer, 2018: 3-11. [5] HE K M, ZHANG X Y, REN S Q, et al. Deep Residual Learning for Image Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 770-778. [6] ZHAO H S, SHI J P, QI X J, et al. Pyramid Scene Parsing Network // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA IEEE, 2017: 6230-6239. [7] NI Z L, BIAN G B, ZHOU X H, et al. RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments // Proc of the International Conference on Neural Information Processing. Berlin Germany: Springer, 2019: 139-149. [8] FAN D P, JI G P, ZHOU T, et al. PraNet: Parallel Reverse Attention Network for Polyp Segmentation // Proc of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin Germany: Springer, 2020: 263-273. [9] WANG Z Y, ZOU N, SHEN D G, et al. Non-local U-Nets for Biomedical Image Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(4): 6315-6322. [10] WANG X L, GIRSHICK R, GUPTA A, et al. Non-local Neural Networks // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington USA: IEEE, 2018: 7794-7803. [11] JHA D, SMEDSRUD P H, RIEGLER M A, et al. ResUNet++: An Advanced Architecture for Medical Image Segmentation // Proc of the IEEE International Symposium on Multimedia. Washington USA: IEEE, 2019: 225-230. [12] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848. [13] HU J, SHEN L, ALBANIE S, et al. Squeeze-and-Excitation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023. [14] GU R, WANG G T, SONG T, et al. CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation. IEEE Transactions on Medical Imaging, 2021, 40(2): 699-711. [15] FENG S L, ZHAO H M, SHI F, et al. CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation. IEEE Transactions on Medical Imaging, 2020, 39(10): 3008-3018. [16] DAI D W, DONG C X, XU S H, et al. Ms RED: A Novel Multi-Scale Residual Encoding and Decoding Network for Skin Lesion Segmentation. Medical Image Analysis, 2022, 75. DOI: 10.1016/j.media.2021.102293. [17] GUO J N, ZENG W, YU S, et al. RAU-Net: U-Net Model Based on Residual and Attention for Kidney and Kidney Tumor Segmentation // Proc of the IEEE International Conference on Consumer Electronics and Computer Engineering. Washington USA: IEEE, 2021: 353-356. [18] CAUSEY J, STUBBLEFIELD J, QUALLS J, et al. An Ensemble of U-Net Models for Kidney Tumor Segmentation with CT Images. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022, 19(3): 1387-1392. [19] RUAN Y N, LI D W, MARSHALL H, et al. MB-FSGAN: Joint Segmentation and Quantification of Kidney Tumor on CT by the Multi-branch Feature Sharing Generative Adversarial Network. Medical Image Analysis, 2020, 64. DOI: 10.1016/j.media.2020.101721. [20] GAL Y, GHAHRAMANI Z.Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning // Proc of the 33rd International Conference on Machine Learning. San Diego USA: JMLR, 2016: 1050-1059. [21] LI Y X, CHEN J W, XIE X P, et al. Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation // Proc of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin Germany: Springer, 2020: 614-623. [22] LUO X D, WANG G T, LIAO W J, et al. Semi-Supervised Medical Image Segmentation via Uncertainty Rectified Pyramid Consistency. Medical Image Analysis, 2022, 80. DOI: 10.1016/j.media.2022.102517. [23] YU L Q, WANG S J, LI X M, et al. Uncertainty-Aware Self-Ensembling Model for Semi-Supervised 3D Left Atrium Segmentation // Proc of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Sprin-ger, 2019: 605-613. [24] CZOLBE S, ARNAVAZ K, KRAUSE O, et al. Is Segmentation Uncertainty Useful? // Proc of the International Conference on Information Processing in Medical Imaging. Berlin, Germany: Sprin-ger, 2021: 715-726. [25] LIU S T, HUANG D, WANG Y H.Receptive Field Block Net for Accurate and Fast Object Detection // Proc of the European Conference on Computer Vision. Berlin,Germany: Springer, 2018: 404-419. [26] CHAURASIA A, CULURCIELLO E.LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation // Proc of the IEEE Visual Communications and Image Processing. Washington USA: IEEE, 2017. DOI: 10.1109/VCIP.2017.8305148. [27] ZHAO Q J, SHENG T, WANG Y T, et al. M2Det: A Single-Shot Object Detector Based on Multi-level Feature Pyramid Network. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 9259-9266. [28] WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional Block Attention Module // Proc of the European Conference on Computer Vision. Berlin, Germany Springer, 2018: 3-19. [29] HWANG G, YOON H, JI Y, et al. RBCA-Net: Reverse Boundary Channel Attention Network for Kidney Tumor Segmentation in CT Images // Proc of the 13th International Conference on Information and Communication Technology Convergence. Washington USA: IEEE, 2022: 2114-2117.
2023, Vol. 36 
