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              基于序列注意力和局部相位引導的骨超聲圖像分割網絡

              陳芳 張道強 廖洪恩 趙喆

              陳芳, 張道強, 廖洪恩, 趙喆. 基于序列注意力和局部相位引導的骨超聲圖像分割網絡. 自動化學報, 2024, 50(5): 970?979 doi: 10.16383/j.aas.c210298
              引用本文: 陳芳, 張道強, 廖洪恩, 趙喆. 基于序列注意力和局部相位引導的骨超聲圖像分割網絡. 自動化學報, 2024, 50(5): 970?979 doi: 10.16383/j.aas.c210298
              Chen Fang, Zhang Dao-Qiang, Liao Hong-En, Zhao Zhe. Bone ultrasound segmentation network based on sequential attention and local phase guidance. Acta Automatica Sinica, 2024, 50(5): 970?979 doi: 10.16383/j.aas.c210298
              Citation: Chen Fang, Zhang Dao-Qiang, Liao Hong-En, Zhao Zhe. Bone ultrasound segmentation network based on sequential attention and local phase guidance. Acta Automatica Sinica, 2024, 50(5): 970?979 doi: 10.16383/j.aas.c210298

              基于序列注意力和局部相位引導的骨超聲圖像分割網絡

              doi: 10.16383/j.aas.c210298
              基金項目: 國家自然科學基金(U20A20389, 61901214), 中國博士后科學基金(2021T140322, 2020M671484)資助
              詳細信息
                作者簡介:

                陳芳:南京航空航天大學計算機科學與技術學院副教授. 主要研究方向為醫學圖像處理, 計算機輔助手術導航. 本文通信作者. E-mail: chenfang@nuaa.edu.cn

                張道強:南京航空航天大學計算機科學與技術學院教授. 主要研究方向為醫學圖像處理, 機器學習. E-mail: dqzhang@nuaa.edu.cn

                廖洪恩:清華大學醫學院教授. 主要研究方向為三維成像, 微創診療. E-mail: liao@tsinghua.edu.cn

                趙喆:清華大學附屬北京清華長庚醫院骨科與運動醫學中心副主任醫師, 清華大學臨床醫學院副教授. 主要研究方向為創傷骨科, 計算機導航手術, 骨科植入物設計. E-mail: zhaozhao_02@163.com

              Bone Ultrasound Segmentation Network Based on Sequential Attention and Local Phase Guidance

              Funds: Supported by National Natural Science Foundation of China (U20A20389, 61901214) and China Postdoctoral Science Foundation (2021T140322, 2020M671484)
              More Information
                Author Bio:

                CHEN Fang Associate professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. Her research interest covers medical image processing and image-guided surgery. Corresponding author of this paper

                ZHANG Dao-Qiang Professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. His research interest covers medical image processing and machine learning

                LIAO Hong-En Professor at the School of Medicine, Tsinghua University. His research interest covers 3D image and minimally invasive diagnosis and therapy

                ZHAO Zhe Associate chief physician of the Orthopaedics and Sports Medicine Center, Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital and associate professor at the School of Clinical Medicine, Tsinghua University. His research interest covers orthopaedic trauma, computer assisted surgery, and orthopaedics implant development

              • 摘要: 在超聲輔助的骨科手術導航中, 需要從采集的超聲圖像序列中精確分割出骨結構, 并展示給醫生, 來輔助醫生進行術中決策. 但是, 圖像噪聲、成像偽影以及模糊的骨邊界導致從超聲圖像序列中精確分割提取骨結構十分困難. 為解決該問題, 提出一種新的基于序列注意力與局部相位引導的骨超聲圖像分割網絡. 該網絡一方面自適應地利用超聲序列幀之間的關系即序列注意力來輔助骨結構的語義分割. 另一方面, 該網絡通過引入局部相位引導模塊, 突出骨邊緣信息, 進一步提高分割精度. 利用包含19 050幅圖像的骨超聲數據集, 進行交叉實驗、消融實驗并與最新的超聲骨分割方法進行比較. 實驗結果表明所提方法對骨結構分割精度高, 優于現有的超聲骨分割方法.
              • 圖  1  基于序列注意力與局部相位引導的骨超聲圖像分割網絡系統框圖; 圖中ConvA表示卷積核為1×1 、步長為1的卷積操作; ConvB表示卷積核為3×3、 步長為1的卷積操作

                Fig.  1  Bone ultrasound segmentation network based on sequential attention and local phase guidance; ConvA denotes the convolution operation with kernel size of 1×1 and a stride of 1; ConvB denotes the convolution operation with kernel size of 3×3 and a stride of 1

                圖  2  骨超聲圖像序列采集示意圖

                Fig.  2  Diagram of bone ultrasound sequence acquisition

                圖  3  10折交叉實驗中訓練和測試超聲圖像幀數的分布

                Fig.  3  The distributions of training and testing frames in 10-fold validation experiments

                圖  4  10次交叉實驗的交并比IoU值的箱線圖

                Fig.  4  Boxplots for IoU values in 10-fold validation experiments

                圖  5  利用所提出的分割網絡對骨結構進行分割的實際結果(第1行: 待分割的骨超聲圖像;第2行: 專家手動標注的骨結構; 第3行: 利用所提出的方法自動分割的骨結構)

                Fig.  5  Bone segmentation results by using the proposed segmentation network (The first line: Ultrasound bone images to be segmented; The second line: Bone structures manually delineated by experts; The third line: Segmented bone structures using the proposed method)

                圖  6  消融實驗結果(本文所提出的分割網絡與兩個變體模型的骨分割實驗結果比較)

                Fig.  6  Ablation results (Comparison between our proposed network and two variants)

                圖  7  局部相位模塊消融實驗結果示例. 第1幀: 超聲圖像幀; 第2幀: 手動標注的骨結構;第3幀: 帶有局部相位模塊的模型分割結果; 第4幀: 去除局部相位模塊的模型分割結果

                Fig.  7  Ablation results of local phase guidance (The first graph: Ultrasound image frame; The second graph: Manually delineated bone structures; The third graph: Results of the model with local phase guidance; The fourth graph: Results of the model without local phase guidance)

                表  1  本研究采集的超聲圖像序列數據集的信息

                Table  1  Information of the ultrasound image sequence dataset collected in this study

                志愿者ID圖像幀數量圖像分辨率(mm/像素)
                11 9000.19 ~ 0.21
                22 0100.17 ~ 0.21
                31 7400.19 ~ 0.21
                41 8700.21 ~ 0.23
                52 0100.19 ~ 0.23
                61 9800.17 ~ 0.21
                71 5600.18 ~ 0.23
                81 9800.19 ~ 0.21
                91 9000.21 ~ 0.23
                102 1000.17 ~ 0.23
                下載: 導出CSV

                表  2  所提出的分割網絡對10名志愿者采集的超聲序列圖像的分割結果

                Table  2  Results of our proposed model obtained on the ultrasound images from ten volunteers

                Exp_K交并比 IoU平均歐氏距離 AED
                Exp_10.91 ± 0.080.41 ± 0.06
                Exp_20.90 ± 0.080.39 ± 0.07
                Exp_30.91 ± 0.070.39 ± 0.06
                Exp_40.92 ± 0.070.37 ± 0.05
                Exp_50.90 ± 0.070.41 ± 0.08
                Exp_60.89 ± 0.080.43 ± 0.08
                Exp_70.91 ± 0.060.42 ± 0.07
                Exp_80.90 ± 0.070.41 ± 0.09
                Exp_90.92 ± 0.060.40 ± 0.07
                Exp_100.91 ± 0.080.39 ± 0.09
                下載: 導出CSV

                表  3  針對主干網絡的比較實驗結果

                Table  3  Comparison experiments by using different backbones

                主干網絡ResNet18ResNet34ResNet50ResNet101VGGNet
                超聲圖像骨分割平均交并比IoU值0.89 ± 0.100.90 ± 0.080.91 ± 0.070.89 ± 0.090.89 ± 0.09
                下載: 導出CSV

                表  4  本文所提出的超聲圖像分割網絡與其他最新的分割方法的實驗結果(IoU值)比較

                Table  4  Comparison of the experimental results (IoU values) of the ultrasound image segmentation network proposed in this study with other state-of-the-art segmentation methods

                實驗結果/
                方法
                局部相位引導
                CNN
                BoneNet模型濾波層引導
                CNN
                時空CNN注意引導網絡
                AGNet
                三重注意力網絡
                TriANet
                本文方法
                Exp_10.880.880.870.850.850.860.91
                Exp_20.870.880.860.840.850.850.90
                Exp_30.870.890.870.860.860.880.91
                Exp_40.890.890.870.850.860.870.92
                Exp_50.870.890.850.860.870.870.90
                Exp_60.860.880.850.840.850.850.89
                Exp_70.860.900.860.840.860.870.91
                Exp_80.870.880.840.850.860.860.90
                Exp_90.890.910.890.860.870.880.92
                Exp_100.870.890.870.870.870.890.91
                平均值0.87 ± 0.130.88 ± 0.080.86 ± 0.140.87 ± 0.120.86 ± 0.090.88 ± 0.100.91 ± 0.07
                下載: 導出CSV
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