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              一種邊界增強的醫學圖像小樣本分割網絡

              賈熹濱 郭雄 王珞 楊大為 楊正漢

              賈熹濱, 郭雄, 王珞, 楊大為, 楊正漢. 一種邊界增強的醫學圖像小樣本分割網絡. 自動化學報, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c220994
              引用本文: 賈熹濱, 郭雄, 王珞, 楊大為, 楊正漢. 一種邊界增強的醫學圖像小樣本分割網絡. 自動化學報, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c220994
              Jia Xi-Bin, Guo Xiong, Wang Luo, Yang Da-Wei, Yang Zheng-Han. A few-shot medical image segmentation network with iterative boundary refinement. Acta Automatica Sinica, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c220994
              Citation: Jia Xi-Bin, Guo Xiong, Wang Luo, Yang Da-Wei, Yang Zheng-Han. A few-shot medical image segmentation network with iterative boundary refinement. Acta Automatica Sinica, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c220994

              一種邊界增強的醫學圖像小樣本分割網絡

              doi: 10.16383/j.aas.c220994
              基金項目: 國家重點研發項目中國和韓國政府間聯合研究項目(2019YFE0107800), 國家自然科學基金(62171298, 82071876) 資助
              詳細信息
                作者簡介:

                賈熹濱:北京工業大學信息學部教授. 主要研究方向為視覺信息認知與計算, 智能醫學圖像分析和診斷, 情感計算. 本文通信作者. E-mail: jiaxibin@bjut.edu.cn

                郭雄:北京工業大學信息學部碩士研究生. 主要研究方向為計算機視覺, 醫學圖像分割, 小樣本學習. E-mail: guox@emails.bjut.edu.cn

                王珞:北京工業大學信息學部講師. 主要研究方向為圖像檢索, 深度學習, 醫學影像處理以及多模態數據融合. E-mail: wangluo@bjut.edu.cn

                楊大為:首都醫科大學附屬北京友誼醫院副教授. 2020年獲得首都醫科大學博士學位. 主要研究方向為肝臟疾病影像研究. E-mail:yangdawei@ccmu.edu.cn

                楊正漢:首都醫科大學附屬北京友誼醫院教授. 1999年獲得北京醫科大學博士學位. 主要研究方向為腹部疾病影像診斷, 肝細胞癌及癌前病變的早期影像診斷, MRI新技術的開發與應用. E-mail: yangzhenghan@vip.163.com

              A Few-shot Medical Image Segmentation Network With Iterative Boundary Refinement

              Funds: Supported by National Key Research and Development Program of China and South Korea Intergovernmental Joint Research Project (2019YFE0107800), National Natural Science Foundation of China (62171298, 82071876)
              More Information
                Author Bio:

                JIA Xi-Bin Professor at Faculty of Information Technology, Beijing University of Technology. Her research interest covers visual information cognition and computing, intelligent medical image analysis and diagnosis and emotional calculation. Corresponding author of this paper

                GUO Xiong Master student at the Faculty of Information Technology, Beijing University of Technology. His research interest covers computer vision, medical image segmentation and few-shot learning

                LUO Wang Lecturer at the Faculty of Information Technology, Beijing University of Technology. His research interests include image retrieval, deep learning, medical image processing and multi-modal data fusion

                YANG Da-Wei Vice professor of Beijing Friendship Hospital, Capital Medical University. He received his Ph.D degree from Capital Medical University in 2020. His main research interest is imaging diagnosis and research on liver disease

                YANG Zheng-Han Professor of Beijing Friendship Hospital, Capital Medical University. He received his Ph.D degree from Beijing Medical University in 1999. His research interests include imaging diagnosis of abdominal diseases, early imaging diagnosis of hepatocellular carcinoma and precancerous lesions, and development and application of new MRI technology

              • 摘要: 精準的醫學圖像自動分割是臨床影像學診斷和影像三維重建的重要基礎.但醫學圖像數據的目標對象間對比度差異小、受器官運動影響大, 加之標注樣本規模小, 因此在小樣本下建立高性能的醫學分割模型仍是目前的難點問題. 針對主流原型學習小樣本分割網絡對醫學圖像邊界分割性能差的問題, 提出一種迭代邊界優化的小樣本分割網絡(Iterative boundary refinement based few-shot-segmentation network, IBR-FSS-Net). 以雙分支原型學習的小樣本分割框架為基礎引入類別注意力機制和密集比較模塊, 對粗分割掩碼進行迭代優化, 引導分割模型在多次迭代學習過程中關注邊界, 從而提升邊界分割精度. 為進一步克服醫學圖像訓練樣本少且多樣性不足的問題, 使用超像素方法生成偽標簽, 擴充訓練數據以提升模型泛化性. 在主流的ABD-MR和ABD-CT醫學圖像分割公共數據集上進行實驗, 與現有多種先進的醫學圖像小樣本分割方法進行了廣泛的對比分析和消融實驗. 結果表明, 該方法有效提升了未見醫學類別的分割性能.
                1)  本文責任編委 XXX Recommended by Associate Editor BIAN Wei
              • 圖  1  基于注意力機制的邊界優化模塊

                Fig.  1  Boundary refinement module based on attention mechanism

                圖  2  基于注意力機制的邊界優化模塊

                Fig.  2  Boundary refinement module based on attention mechanism

                圖  3  原型網絡結構圖

                Fig.  3  Prototype network structure diagram

                圖  4  密集比較模塊

                Fig.  4  Dense comparison module

                圖  5  基于超像素的樣本擴充方法

                Fig.  5  Self-supervised learning process based on superpixel algorithm

                圖  6  數據集劃分示意圖

                Fig.  6  ABD-MR dataset partition diagram

                圖  7  醫學圖像小樣本分割網絡的Baseline模型

                Fig.  7  Baseline model of few-shot medical image segmentation network

                圖  8  核磁共振成像圖像中的四種器官樣例的預測掩碼

                Fig.  8  Prediction masks for four organ samples in magnetic resonance images

                圖  9  電子計算機斷層掃描圖像中的四種器官樣例的預測掩碼

                Fig.  9  Prediction masks for four organ samples in computed tomography images

                表  1  在 ABD-CT 與 ABD-MR 數據集上不同模型的量化分割結果(%)

                Table  1  Quantitative segmentation results of different models on ABD-CT and ABD-MR datasets (%)

                方法 ABD-CT數據集 ABD-MR數據集
                脾臟 左腎 右腎 肝臟 平均值 脾臟 左腎 右腎 肝臟 平均值
                SE-Net 0.23 32.83 14.34 0.27 11.91 51.80 62.11 61.32 27.43 50.66
                PANet 25.59 32.34 17.37 38.42 29.42 50.90 53.45 38.64 42.26 46.33
                ALPNet 60.25 63.34 54.82 73.65 63.02 67.02 73.63 78.39 73.05 73.02
                GCN-DE 56.53 68.13 75.50 46.77 61.73 60.63 76.07 83.03 49.47 67.30
                RP-Net 69.85 70.48 70.00 79.62 72.48 76.35 81.40 85.78 73.51 79.26
                ADNet 75.92 75.28 83.28 80.81 78.82
                PoissonSeg 52.33 50.11 47.02 58.74 52.05 52.85 50.58 53.57 61.03 54.51
                AAS-DCL 66.36 64.71 69.95 71.61 68.16 74.86 76.90 83.75 69.94 76.36
                IBR-FSS-Net 71.73 73.78 72.02 78.13 73.92 75.12 82.19 85.64 75.89 79.71
                下載: 導出CSV

                表  2  不同組件的量化分割結果(%)

                Table  2  Quantified segmentation results for different components (%)

                組件 脾臟 左腎 右腎 肝臟 平均值
                Baseline 62.33 63.65 66.87 64.18 64.26
                Baseline+Concat 60.62 65.55 68.53 66.56 65.32
                Baseline+BRM 66.63 72.10 74.83 69.17 70.68
                Baseline+3Concat 61.20 68.72 70.38 66.95 66.81
                Baseline+3BRM 75.12 82.19 85.64 75.89 79.71
                下載: 導出CSV

                表  3  邊界優化模塊的數量的影響(%)

                Table  3  The effect of the number of boundary refinement modules (%)

                組件 脾臟 左腎 右腎 肝臟 平均值
                Baseline 62.33 63.65 66.87 64.18 64.26
                Baseline+BRM 66.63 72.10 74.83 69.17 70.68
                Baseline+2BRM 69.88 79.98 82.12 73.56 76.39
                Baseline+3BRM 75.12 82.19 85.64 75.89 79.71
                Baseline+4BRM 68.57 77.23 78.82 69.60 73.56
                Baseline+5BRM 64.13 70.55 72.69 66.42 68.45
                下載: 導出CSV

                表  4  不同特征提取網絡的量化分割結果(%)

                Table  4  Quantified segmentation results of different feature extraction networks (%)

                骨干網絡 脾臟 左腎 右腎 肝臟 平均值
                VGG-16 52.09 63.83 64.48 57.88 59.57
                U-Net 69.66 78.94 80.46 72.15 75.30
                Res U-Net 71.82 78.24 81.10 73.41 76.14
                Attention U-Net 73.96 79.14 83.51 73.60 77.55
                ResNet-50 71.23 78.19 82.57 73.68 76.42
                ResNet-101 75.12 82.19 85.64 75.89 79.71
                下載: 導出CSV

                表  5  度量網絡組合方式的影響(%)

                Table  5  The effect of the combination of metric network (%)

                原型網絡拼接方式 脾臟 左腎 右腎 肝臟 平均值
                Prototypical-Net 70.92 80.61 83.70 74.48 77.43
                DCM 71.53 81.36 83.44 74.81 77.79
                DCM + Prototypical-Net 72.97 81.49 83.68 74.83 78.24
                Prototypical-Net +DCM 75.12 82.19 85.64 75.89 79.71
                下載: 導出CSV

                表  6  與使用半監督、對比學習、弱監督的醫學分割模型在 ABD-CT 與 ABD-MR 數據集上結果對比(%)

                Table  6  Comparison of results with medical segmentation models ssing semi-supervised, comparative learning, and weakly supervised on ABD-CT and ABD-MR datasets (%)

                方法(比例) ABD-CT數據集 ABD-MR數據集
                脾臟 左腎 右腎 肝臟 平均值 脾臟 左腎 右腎 肝臟 平均值
                MagicNet(30%) 91.42 86.19 84.64 93.89 89.04
                CVCL(部分) 95.40 94.60 94.60 96.70 95.33
                C-CAM(0%) 74.16 81.00 84.75 72.68 78.15
                IBR-FSS-Net(5%) 71.73 73.78 72.02 78.13 73.92 75.12 82.19 85.64 75.89 79.71
                下載: 導出CSV
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                      1. [1] Zhang B, Zhang L, Zhang L, Karray F. Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Computers in Biology and Medicine, 2010, 40(4): 438-445 doi: 10.1016/j.compbiomed.2010.02.008
                        [2] Vinyals O, Blundell C, Lillicrap T, Wierstra D. Matching networks for one shot learning. In: Proceedings of the PMLR on Advances in Neural Information Processing Systems. Barcelona, Spain: PMLR, 2016. 29
                        [3] Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the PMLR on International Conference on Machine Learning. Sydney, Australia: PMLR, 2017. 1126?1135
                        [4] Sung F, Yang Y, Zhang L, Tao X, Philip H S T, Timothy M. Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE on Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018. 1199?1208
                        [5] Li G, Jampani V, Sevilla-Lara L, Sun D, Kim Jonghyun, Joongkyu Kim. Adaptive prototype learning and allocation for few-shot segmentation. In: Proceedings of the IEEE on Conference on Computer Vision and Pattern Recognition. Virtual Event: IEEE, 2021. 8334?8343
                        [6] Yu Q, Dang K, Tajbakhsh N, et al. A location-sensitive local prototype network for few-shot medical image segmentation. In: Proceedings of the IEEE on Image Processing. Anchorage, Alaska, USA: IEEE, 2021. 262?266
                        [7] 孫君梅, 葛青青, 李秀梅, 趙寶奇. 具有邊界增強功能的醫學圖像分割網絡. 電子與信息學報, 2022, 44(5): 1643-1652 doi: 10.11999/JEIT210784

                        Sun Jun-Mei, Ge Qing-Qing, Li Xiu-Mei, Zhao Bao-Qi. A medical image segmentation network with boundary enhancement. Journal of Electronics, 2022, 44(5): 1643-1652. doi: 10.11999/JEIT210784
                        [8] Yuan Y, Chen X, Wang J. Object-contextual representations for semantic segmentation. In: Proceedings of the European Conference on Computer Vision. Virtual Event: Springer, 2020. 23-28, 173?190
                        [9] Kim T, Lee H, Kim D. Uacanet: Uncertainty augmented context attention for polyp segmentation. In: Proceedings of the IEEE Conference on ACM International Conference on Multimedia. Chengdu, China: ACM, 2021. 2167?2175
                        [10] Zhang C, Lin G, Liu F, Rui Y, Chunhua S. Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In: Proceedings of the IEEE Conference on Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019. 5217?5226
                        [11] Ouyang C, Biffi C, Chen C, Kart T, Qiu H, Rueckert D. Self-supervision with superpixels: Training few-shot medical image segmentation without annotation. In: Proceedings of the European Conference on Computer Vision. Virtual Event: Springer, 2020. 762?780
                        [12] Tang H, Liu X, Sun S, Yan X, Xie X. Recurrent mask refinement for few-shot medical image segmentation. In: Proceedings of IEEE on International Conference on Computer Vision: Seoul, South Korea: IEEE, 2021. 3918?3928
                        [13] Fan D P, Ji G P, Zhou T, Chen G, Fu H, Shen J, et al. Pranet: Parallel reverse attention network for polyp segmentation. In: Proceedings of the Medical Image Computing and Computer Assisted Intervention. Lima, Peru: Springer, 2020. 263?273
                        [14] Shaban A, Bansal S, Liu Z, Essa I, Byron B. One-shot learning for semantic segmentation. In: Proceedings of the IEEE on Computer Vision and Pattern Recognition. Hawaii, USA: IEEE, 2017.
                        [15] Wang K, Liew J H, Zou Y, Zhou D, Feng J. Panet: Few-shot image semantic segmentation with prototype alignment.In: Proceedings of the IEEE on International Conference on Computer Vision. Seoul, South Korea: IEEE, 2019. 9197?9206
                        [16] Tian Z, Zhao H, Shu M, Yang Z, Li R, Jia J. Prior guided feature enrichment network for few-shot segmentation. Pattern Analysis and Machine Intelligence, 2020. 44(2): 1050-1065
                        [17] Zhang G, Kang G, Yang Y, Wei Y. Few-shot segmentation via cycle-consistent transformer. In: Proceedings of the Advances in Neural Information Processing Systems. Montreal Canada: NeurIPS, 2021. 21984?21996
                        [18] Roy A G, Siddiqui S, Polsterl S, Navab N, Wachinger C. “Squeeze & excit” guided few-shot segmentation of volumetric images. Medical Image Analysis, 2020, 59: Article No. 101587
                        [19] Sun L, Li C, Ding X, Huang Y, Chen Z, Wang G, et al. Few-shot medical image segmentation using a global correlation network with discriminative embedding. Computers in Biology an Medicine, 2022, 140: Article No. 105067
                        [20] Tang H, Liu X, Sun S, Yan X, Xie X. Recurrent mask refinement for few-shot medical image segmentation. In: Proceedings of the IEEE on International Conference on Computer Vision. Montreal, Canada: IEEE, 2021. 3918?3928
                        [21] Hansen S, Gautam S, Jenssen R, Kampffmeyer M. Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels. Medical Image Analysis, 2022, 78: Article No. 102385
                        [22] Shen X, Zhang G, Lai H, et al. PoissonSeg: Semi-supervised few-shot medical image segmentation via poisson learning. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine. Houston, USA: IEEE, 2021. 1513?1518
                        [23] Wu H, Xiao F, Liang C. Dual Contrastive learning with anatomical auxiliary supervision for few-shot medical image segmentation. In: Proceedings of the European Conference on Computer Vision. Tel Aviv, Israel: Springer, 2022. 417?434
                        [24] Chen D, Bai Y, Shen W, Li Q, Yu L, Wang Y. MagicNet: Semi-supervised multi-Organ segmentation via magic-cube partition and recovery. In: Proceedings of the IEEE on Computer Vision and Pattern Recognition. Oxford, UK: IEEE, 2023. 23869?23878
                        [25] Liu P, Zheng G. Context-aware voxel-wise contrastive learning for label efficient multi-organ segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Singapore: Springer, 2022. 653?662
                        [26] Chen Z, Tian Z, Zhu J, Li C, Du S. C-cam: Causal cam for weakly supervised semantic segmentation on medical image. In: Proceedings of the IEEE on Computer Vision and Pattern Recognition. New Orleans, USA: IEEE, 2022. 11676?11685
                        [27] Chen S, Tan X, Wang B, Hu X. Reverse attention for salient object detection. In: Proceedings of the European Conference on Computer Vision. Munich, Germany: Springer, 2018. 234?250
                        [28] 陳瓊, 楊永, 黃田琳, 馮媛. 新型全向立體視覺系統設計. 數據與計算發展前沿, 2022, 3(6): 17-34

                        Chen Qiong, Yang Yong, Huang Tian-Lin, Feng Yuan. A survey on few-shot image semantic segmentation. Frontiers of Data and Domputing, 2022, 3(6): 17-34.
                        [29] Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning. In: Proceedings of the Conference on Neural Information Processing Systems. Long Beach, USA: NeurIPS, 2017. 30
                        [30] Landman B, Xu Z, Igelsias J, Styner M, Langerak T, Klein A. Miccai multi-atlas labeling beyond the cranial vault-workshop and challenge.In: Proceedings of the Medical Image Computing and Computer Assisted Intervention. Munich, Germany: Springer, 2015. 12
                        [31] Kavur A E, Gezer N S, Baris M, Aslan S, Conze P H, Groza V. CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis, 2021, 69: Atricle No. 101950
                        [32] Irving B. MaskSLIC: regional superpixel generation with application to local pathology characterisation in medical images. In: Proceedings of the IEEE on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 09518
                        [33] Lin T Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, et al. Microsoft coco: Common objects in context.In: Proceedings of the European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 740?755
                        [34] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the IEEE on Computer Vision and Pattern Recognition. Columbus, USA: IEEE, 2014. 1409. 1556
                        [35] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Proceedings of the Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer, 2015. 234?241
                        [36] Xiao X, Lian S, Luo Z, Li S. Weighted res-unet for high-quality retina vessel segmentation. In: Proceedings of the International Conference on Information Technology in Medicine and Education. Hangzhou, China: IEEE, 2018. 327?331
                        [37] Oktay O, Schlemper J, Folgoc L L, Lee M, Heinrich M, Misawa K. Attention U-net: Learning where to look for the pancreas. In: Proceedings of the IEEE on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018. 1804. Atricle No. 03999
                        [38] Kervadec H, Bouchtiba J, Desrosiers C, Granger E, Dolz J, Ayed I.B. Boundary loss for highly unbalanced segmentation.In: Proceedings of the International Conference on Medical Imaging with Deep Learning. Shanghai, China: PMLR, 2019: 285?296
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